Programme booklet (pdf)

Ghent, February 11 th 2011
21 st meeting of Computational
Linguistics In the Netherlands

CLIN-21 was supported by:

Welcome!
For the first time in its over 20-year history, the “Computational Linguistics in the
Netherlands” meeting is being held in beautiful city of Ghent. This year’s edition of the
meeting is hosted by the Language and Translation Technology Team of the University
College Ghent.
CLIN-21 will cover a broad spectrum of areas related to natural language and
computation. The program features 55 talks, organized in 5 parallel sessions and 18
posters on different aspects of computational linguistics. We are delighted that
Massimo Poesio from the University of Essex accepted to give us his vision on current
anaphora resolution research. Leonoor van der Beek will present a booklet on the
history of language and speech technology in the Netherlands and Flanders. At the
CLIN meeting, we will also present the winner of the STIL Thesis Prize 2011 that will be
awarded to the best MA thesis in computational linguistics or its applications.
This booklet contains the presentation and poster abstracts for this year’s CLIN, as well
as the program schedule. The abstracts have been ordered alphabetically.
We hope that CLIN-21 will provide a rewarding forum for the presentation of
interesting work and new ideas in the domain of computational linguistics and natural
language processing, with stimulating and provocative discussions of successes, failures
and new directions. We thank you for your support and participation and wish you a
pleasant and fruitful conference!
The CLIN-21 organizing committee
Veronique Hoste
Els Lefever
Kathelijne Denturck
Peter Velaerts
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Table of Contents
Welcome! ......................................................................................................................... 3
Table of Contents ............................................................................................................. 5
Programme ....................................................................................................................... 9
Keynote speaker ............................................................................................................. 17
Rethinking anaphora .................................................................................................. 17
Presentation Abstracts ................................................................................................... 19
A discriminative syntactic model for source permutation via tree transduction for
statistical machine translation .................................................................................... 20
A Generalized Lexical Acquisition Technique for Improved Parsing Accuracy ............ 21
A Semantic Vector Space for Modelling Word Meaning in Context ........................... 22
A Toolkit for Visualizing the Coherence of Tree-based Reordering with Word-
Alignments .................................................................................................................. 23
A U-DOP approach to modeling language acquisition ................................................ 24
Age and Gender Prediction on Netlog Data................................................................ 25
Aligning translation divergences through semantic role projection ........................... 26
An Aboutness-based Dependency Parser for Dutch ................................................... 27
An exploration of n-gram relationships for transliteration identification .................. 28
Application of a Constraint Conditional Model for improving the performance of
a Sequence Tagger : A Case Study .............................................................................. 29
Automatic terminology extraction: methods and practical applications ................... 30
Automatically Constructing a Wordnet for Dutch ...................................................... 31
Automatically determining phonetic distances .......................................................... 32
Building a Gold Standard for Dutch Spelling Correction ............................................. 33
Clustering customer questions ................................................................................... 34
Collecting and using a corpus of lyrics and their moods ............................................. 35
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CLIN 21 – CONFERENCE PROGRAMME
Combined Qualitative and Quantitative Error Analysis in Multi-Topic Authorship
Attribution ................................................................................................................. 36
Combining e-learning and natural language processing. The example of
automated dictation exercises .................................................................................. 37
Computing Semantic Relations from Heterogeneous Information Sources .............. 38
Computing the meaning of multi-word expressions for semantic inference ............ 39
Cross-Domain Dutch Coreference Resolution ........................................................... 40
Dmesure: a readability platform for French as a foreign language ........................... 42
Essentials of person names ........................................................................................ 43
Extraction of Historical Events from Unstructered Texts ........................................... 44
Finding Statistically Motivated Features Influencing Subtree Alignment
Performance .............................................................................................................. 45
From Tokens to Text Entities: Line-based Parsing of Resumes using Conditional
Random Fields ........................................................................................................... 46
Im chattin :-) u wanna NLP it: Analyzing Reduction in Chat ....................................... 47
Language Evolution and SA-OT: The case of sentential negation .............................. 48
Machine Learning Approaches to Sentiment Analysis Using the Dutch Netlog
Corpus ........................................................................................................................ 49
Measuring the Impact of Controlled Language on Machine Translation Text via
Readability and Comprehensibility ............................................................................ 50
Memory-based text completion ................................................................................ 51
Overlap-based Phrase Alignment for Language Transformation ............................... 52
Parse and Tag Somali Pirates ..................................................................................... 53
Personalized Knowledge Discovery: Combining Social Media and Domain
Ontologies.................................................................................................................. 54
Recent Advances in Memory-Based Machine Translation ........................................ 55
Reversible stochastic attribute-value grammars ....................................................... 56
Robust Rhymes? The Stability of Authorial Style in Medieval Narratives .................. 57

TABLE OF CONTENTS
Rule Induction for Synchronous Tree-Substitution Grammars in Machine
Translation .................................................................................................................. 58
Search in the Lassy Small Corpus ................................................................................ 59
Simple Measures of Domain Similarity for Parsing ..................................................... 60
SSLD: A smart tool for sms compression .................................................................... 61
Subtrees as a new type of context in Word Space Models......................................... 62
Successful extraction of opposites by means of textual patterns with part-of-
speech information only. ............................................................................................ 63
Syntactic Analysis of Dutch via Web Services ............................................................. 64
Technology recycling between Dutch and Afrikaans .................................................. 65
Technology recycling for closely related languages: Dutch and Afrikaans ................. 66
The more the merrier? How data set size and noisiness affect the accuracy of
predicting the dative alternation ................................................................................ 67
The use of structure discovery methods to detect syntactic change ......................... 69
Treatments of the Dutch verb cluster in formal and computational linguistics ......... 70
TTNWW: de facto standards for Dutch in the context of CLARIN ............................... 71
TTNWW: NLP Tools for Dutch as Webservices in a Workflow .................................... 72
Using corpora tools to analyze gradable nouns in Dutch. .......................................... 73
Using easy distributed computing for data-intensive processing ............................... 74
What is the use of multidocument spatiotemporal analysis? .................................... 75
Without a doubt no uncomplicated task: Negation cues and their scope ................. 76
Poster Abstracts ............................................................................................................. 77
A database for lexical orthographic errors in French ................................................. 77
A Posteriori Agreement as a Quality Measure for Readability Prediction Systems .... 79
A TN/ITN Framework for Western European languages ............................................ 80
An Examination of Cross-Cultural Similarities and Differences from Social Media
Data with respect to Language Use ............................................................................ 81
Authorship Verification of Quran ............................................................................... 82
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CLIN 21 – CONFERENCE PROGRAMME
CLAM: Computational Linguistics Application Mediator ........................................... 83
Discriminative features in reversible stochastic attribute-value grammars .............. 84
Fietstas: a web service for text analysis ..................................................................... 85
FoLiA: Format for Linguistic Annotation .................................................................... 86
How can computational linguistics help determine the core meaning of then in
oral speech? ............................................................................................................... 87
Of mathematicians and physicists: the history of language and speech technology
in the Netherlands and Flanders ................................................................................ 88
On the difficulty of making concreteness concrete ................................................... 89
ParaSense or how to use parallel corpora for Cross-Lingual Word Sense
Disambiguation .......................................................................................................... 90
"Pattern", a web mining module for Python ............................................................. 91
Semantic role labeling of gene regulation events ...................................................... 92
Source Verification in Quran ...................................................................................... 93
Towards a language-independent data-driven compound decomposition tool ....... 94
Towards improving the precision of a relation extraction system by processing
negation and speculation .......................................................................................... 95
List of Participants ......................................................................................................... 97

Programme
9

CLIN 21 – CONFERENCE PROGRAMME
09:00
-
09:30
Registration and coffee (Foyer)
Room 328 303 313
Chair
09:30
-
9:50
09:50
-
10:10
10:10
-
10:30
10:30
-
10:50
10:50
-
11:10
11:10
-
11:20
11:20
-
11:30
Social media
Richard Beaufort
Age and Gender Prediction
on Netlog Data
C. Peersman, W.
Daelemans, L. Van
Vaerenbergh
Im chattin :-) u wanna NLP
it: Analyzing Reduction in
Chat
H. van Halteren, G.
Martell, C. Du, Y. Gu, J.
Kobben, L. Panjaitan, L.
Schubotz, K. Vasylenko, Y.
Vladimirova
Personalized Knowledge
Discovery: Combining
Social Media and Domain
Ontologies
Th. Markus, E.
Westerhout, P. Monachesi
Machine Learning
Approaches to Sentiment
Analysis Using the Dutch
Netlog Corpus
S. Schrauwen, W.
Daelemans
Syntax and parsing
Menno van Zaanen
The use of structure
discovery methods to
detect syntactic change
L. ten Bosch, M. Versteegh
An Aboutness-based
Dependency Parser for
Dutch
C. H.A. Koster
A Generalized Lexical
Acquisition Technique for
Improved Parsing Accuracy
K. Cholakov, G. van Noord,
V. Kordoni, Y. Zhang
Search in the Lassy Small
Corpus
G. van Noord, D. de Kok, J.
van der Linde
Coffee break.(Foyer)
Welcome (Auditorium)
STIL thesis prize (Auditorium)
Lexical semantics
Tanja Gaustad
A Semantic Vector Space
for Modelling Word
Meaning in Context
K. Heylen, D. Speelman, D.
Geeraerts
Automatically Constructing
a Wordnet for Dutch
T. Van de Cruys
Successful extraction of
opposites by means of
textual patterns with partof-speech
information
only.
A. Lobanova
Computing the meaning of
multi-word expressions for
semantic inference
C. Cremers
10

PROGRAMME
Machine translation
Lieve Macken
317 403
Rule Induction for
Synchronous Tree-
Substitution Grammars in
Machine Translation
V. Vandeghinste, Scott
Martens
Recent Advances in
Memory-Based Machine
Translation
M. van Gompel, A. van den
Bosch, P. Berck
A discriminative syntactic
model for source
permutation via tree
transduction for statistical
machine translation
M. Khalilov, Kh. Sima'an,
G.M. de Buy Wenniger
Measuring the Impact of
Controlled Language on
Machine Translation Text
via Readability and
Comprehensibility
St. Doherty
Methodology
Erik Tjong Kim Sang
Using easy distributed
computing for dataintensive
processing
J. Van den Bogaert
Application of a Constraint
Conditional Model for
improving the
performance of a
Sequence Tagger : A Case
Study
T. Stokman
Technology Recycling
between Dutch and
Afrikaans
L. Augustinus, G. van
Huyssteen, S. Pilon
Technology recycling for
closely related languages:
Dutch and Afrikaans
S. Pilon, G. Van Huyssteen
09:00
-
09:30
09:30
-
9:50
09:50
-
10:10
10:10
-
10:30
10:30
-
10:50
10:50
-
11:10
11:10
-
11:20
11:20
-
11:30
11

11:30
-
12:30
12:30
-
12:50
12
CLIN 21 – CONFERENCE PROGRAMME
Invited talk: Rethinking anaphora
M. Poesio
(Auditorium)
Of mathematicians and physicists: the history of language
and speech technology in the Netherlands and Flanders
L. van der Beek
(Auditorium)
12:50
-
13:50
Lunch break (Restaurant)
Room 328 303 313
Chair
13:50
-
14:10
14:10
-
14:30
14:30
-
14:50
14:50
-
15:10
15:10
-
16:10
Information extraction
Eline Westerhout
Extraction of Historical
Events from Unstructered
Texts
R. Segers, M. van Erp, L.
van der Meij
Clustering customer
questions
F. Nauze
Parse and Tag Somali
Pirates
M. van Erp, V. Malaisé, W.
van Hage, V. Osinga, J.M.
Coleto
From Tokens to Text
Entities: Line-based
Parsing of Resumes using
Conditional Random Fields
M. Rotaru
Syntax and parsing
Antal van den Bosch
Reversible stochastic
attribute-value grammars
D. de Kok, G. van Noord, B.
Plank
The more the merrier?
How data set size and
noisiness affect the
accuracy of predicting the
dative alternation
D. Theijssen, H. van
Halteren, L. Boves, N.
Oostdijk
Simple Measures of
Domain Similarity for
Parsing
B. Plank, G. van Noord
Treatments of the Dutch
verb cluster in formal and
computational linguistics
F. Van Eynde
Poster session & coffee break.(Foyer)
Semantics
Kris Heylen
Computing Semantic
Relations from
Heterogeneous
Information Sources
A. Panchenko
Essentials of person names
M. Schraagen
Subtrees as a new type of
context in Word Space
Models
M. Smets, D. Speelman, D.
Geeraerts
Using corpora tools to
analyze gradable nouns in
Dutch.
N. Ruiz, E. Weiffenbach

PROGRAMME
Translation
Vincent Vandeghinste
317 403
Finding Statistically
Motivated Features
Influencing Subtree
Alignment Performance
G. Kotzé
A Toolkit for Visualizing
the Coherence of Treebased
Reordering with
Word-Alignments
G. Maillette de Buy
Wenniger
Overlap-based Phrase
Alignment for Language
Transformation
S. Wubben, A. van den
Bosch, E. Krahmer
Aligning translation
divergences through
semantic role projection
T. Vanallemeersch
Discourse
Kim Luyckx
Cross-Domain Dutch
Coreference Resolution
O. De Clercq, V. Hoste
What is the use of
multidocument
spatiotemporal analysis?
I. Schuurman, V.
Vandeghinste
Language Evolution and
SA-OT: The case of
sentential negation
A. Lopopolo, T. Biro
Without a doubt no
uncomplicated task:
Negation cues and their
scope
R. Morante, S. Schrauwen,
W. Daelemans
11:30
-
12:30
12:30
-
12:50
12:50
-
13:50
13:50
-
14:10
14:10
-
14:30
14:30
-
14:50
14:50
-
15:10
15:10
-
16:10
13

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CLIN 21 – CONFERENCE PROGRAMME
Room 328 303 313
Standards/CLARIN
Syntax/Spelling
Lexical semantics/
Discourse
Chair Gertjan van Noord
Roser Morante
Tim Van de Cruys
16.10
-
16.30
16.30
-
16.50
16.50
-
17.10
17:10
-
19:00
TTNWW: de facto
standards for Dutch in the
context of CLARIN
I. Schuurman, M. Kemps-
Snijders
TTNWW: NLP Tools for
Dutch as Webservices in a
Workflow
M. Kemps-Snijders, I.
Schuurman
Syntactic Analysis of Dutch
via Web Services
E. Tjong Kim Sang
A U-DOP approach to
modeling language
acquisition
M. Smets
Memory-based text
completion
A. van den Bosch
Building a Gold Standard
for Dutch Spelling
Correction
T. Gaustad, A. van den
Bosch
Drinks (Foyer)
An exploration of n-gram
relationships for
transliteration
identification
P. Nabende
Combined Qualitative and
Quantitative Error Analysis
in Multi-Topic Authorship
Attribution
K. Luyckx, W. Daelemans
Robust Rhymes? The
Stability of Authorial Style
in Medieval Narratives
M. Kestemont, W.
Daelemans, D. Sandra

PROGRAMME
317 403
Beyond text
Tools
Paola Monachesi
Combining e-learning and
natural language
processing. The example of
automated dictation
exercises
R. Beaufort, S. Roekhaut
Combining e-learning and
natural language
processing. The example of
automated dictation
exercises
R. Beaufort, S. Roekhaut
Collecting and using a
corpus of lyrics and their
moods
M. van Zaanen
Els Lefever
Automatic terminology
extraction: methods and
practical applications
D. de Vries
Automatic terminology
extraction: methods and
practical applications
D. de Vries
SSLD: A smart tool for sms
compression
L.-A. Cougnon, R. Beaufort
16.10
-
16.30
16.30
-
16.50
16.50
-
17.10
17:10
-
19:00
15

Rethinking anaphora
Abstract
Massimo Poesio
Keynote speaker
Current models of the anaphora resolution task achieve mediocre results for all but the
simpler aspects of the task such as coreference proper (i.e. linking proper names into
coreference chains). One of the reasons for this state of affairs is the drastically
simplified picture of the task at the basis of existing annotated resources and modelse.g.,
the assumption that human subjects by and large agree on anaphoric judgments.
In this talk I will present the current state of our efforts to collect more realistic
judgments about anaphora through the Phrase Detectives online game, and to develop
models of anaphora resolution that do not rely on the total agreement assumption.
17

Presentation Abstracts
19

20
CLIN 21 – CONFERENCE PROGRAMME
A discriminative syntactic model for source permutation
via tree transduction for statistical machine translation
Abstract
Khalilov, Maxim and Sima'an, Khalil and de Buy Wenniger, Gideon
Maillette
ILLC-UvA
Word ordering is still one of the most challenging problems in the statistical machine
translation (SMT). In most existing work, a word reordering model is implicitly or
explicitly incorporated into a translation system based on flat or hierarchical
representations of phrases. By contrast, this study deals with an approach addressing
word ordering problem via source-side permutation prior to translation using
hierarchical and syntactic structures.
Our work is driven by the idea that reordering the source sentence as a pre-translation
step minimizes the need for reordering during translation and may bridge long-distance
order differences, which are outside the scope of commonly used reordering models.
Given a word-aligned parallel corpus, we define the source string permutation as the
task of statistically learning to unfold the crossing alignments between sentence pairs
in the parallel corpus.
This work contributes an approach for learning source string permutation via transfer
of the source syntax tree, i.e. we define source permutation as the problem of learning
how to transfer a given source parse-tree into a parse-tree that minimizes the
divergence from target word-order.
We present a novel discriminative, probabilistic tree transduction model, and
contribute a set of empirical oracle results (upperbounds on translation performance)
for English-to-Dutch source string permutation under sequence and parse tree
constraints. Finally, the translation performance of our learning model is shown to
outperform the state-of-the-art phrase-based system significantly.
Corresponding author: maxkhalilov@gmail.com

PRESENTATION ABSTRACTS
Abstract
A Generalized Lexical Acquisition Technique for
Improved Parsing Accuracy
Cholakov, Kostadin 1 and van Noord, Gertjan 1 and Kordoni, Valia 2 and
Zhang, Yi 3
1 University of Groningen
2 DFKI, Germany
3 University of Saarland, Germany
Unknown words are a major issue for large-scale precision grammars of natural
language. In Cholakov and van Noord (COLING 2010) we proposed a maximum entropy
based classification algorithm for acquiring lexical entries for all forms in the paradigm
of a given unknown word and we tested its performance on the Dutch Alpino grammar.
The study showed an increase in parsing accuracy when our method was applied.
However, the general applicability of our approach has been a major point of criticism.
It has been considered too specific and its application to other systems and languages--
doubtful.
In this presentation, we argue that our method can be applied to any precision
grammar provided that the following conditions are fulfilled: a finite set of labels which
unknown words are mapped onto, large corpora, a parser which analyses various
contexts of a given unknown word and provides syntactic constraints used as features
in the classification process and a morphological component which generates the
paradigm(s) of the unknown word. We show that the fulfillment of these conditions
allows us to apply successfully our approach to other large-scale grammars where it
leads to a significant increase in parsing accuracy on test sets of sentences containing
unknown words in comparison with the achieved accuracy when the default methods
used by these grammars to handle unknown words are employed.
This provides strong support for our claim that our approach is general enough to be
applied to various languages and precision grammars.
Corresponding author: kcholakov@gmail.com
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22
CLIN 21 – CONFERENCE PROGRAMME
A Semantic Vector Space for Modelling Word Meaning in
Context
Abstract
Heylen, Kris and Speelman, Dirk and Geeraerts, Dirk
QLVL, Katholieke Universiteit Leuven
Semantic Vector spaces have become the mainstay of modelling of word meaning in
statistical NLP. They encode the semantics of words through high-dimensional vectors
that record the co-occurrence of those words with context features in a large corpus.
Vector comparison then allows for the calculation of e.g. semantic similarity between
words. Most semantic vector spaces represent word meaning on the type (or lemma)
level, i.e. their vectors generalize over all occurrences of a word. However, the meaning
of words can differ considerably between contexts due to polysemy or vagueness.
Therefore, many applications, like Word Sense Disambiguation (WSD) or Textual
Entailment, require that word meaning be modelled on the token level, i.e. the level of
individual occurrences. In this paper, we present a semantic vector space model that
represents the meaning of word tokens by taking the word type vector and reweighting
it based on the words observed in the token's immediate vicinity. More specifically,
we give a bigger weight to the context features in the original type vector that are
semantically similar to the context features observed around the token. This semantic
similarity between context features is calculated based on the original wordtype-bycontextfeature
matrix. We explore the performance of this model in a WSD task by
visualizing how well the model separates the different meanings of polysemous words
in Multi-Dimensional Scaling solution. We also compare our model to other token-level
semantic vector spaces as proposed by Schütze (1998) and Erk & Padó (2008).
References
Erk, K. & S. Padó. 2008. A Structured Vector Space Model for Word Meaning in Context.
EMNLP Proceedings, 897-906.
Schütze, H. 1998. Automatic word sense discrimination.Computational Linguistics,
24(1):97–124
Corresponding author: kris.heylen@arts.kuleuven.be

PRESENTATION ABSTRACTS
A Toolkit for Visualizing the Coherence of Tree-based
Reordering with Word-Alignments
Abstract
Maillette de Buy Wenniger, Gideon
Institute for Logic Language and Computation
Tree-based reordering constitutes an important motivation for the increasing interest
in syntax-driven machine translation. It has often been argued that tree-based
reordering might provide a more effective approach for bridging the word-order
differences between source and target sentences. One major approach known as ITG
(Inversion Transduction Grammar) allows permuting the order of the subtrees
dominated by the children of any node in the tree. In practice, it has often been
observed that the word-alignments usually cohere only to a certain degree with this
kind of tree-based reordering, i.e., there are cases of word-alignments that cannot be
fully explained with tree-based reordering when the tree is fixed a priori. This
presentation describes a toolkit for visualizing alignment graphs that consist of a wordalignment
together with a source or target tree. More importantly, the toolkit provides
a facility for visualizing the coherence of word-alignment with tree-based reordering,
highlighting nodes and word-alignments that are incompatible with one another. The
tool allows visualizing the tree-based reordered source/target string as well as the
reordered tree. Using our toolkit, we will also present results pertaining to the
coverage of the ITG assumption of the word-alignments of a Europarl corpus, which is a
very common starting point for training Translation systems. We will also dwell on the
break-down of the types of incompatibility into general classes and discuss what that
implies for training hierarchical translation models on this type of data.
Corresponding author: gemdbw@gmail.coom
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CLIN 21 – CONFERENCE PROGRAMME
A U-DOP approach to modeling language acquisition
Abstract
Smets, Margaux
In linguistics, there is a debate between empiricists and nativists:
the former believe that language is acquired from experience, the latter that there is
an innate component for language. The main arguments adduced by nativists are
Arguments from Poverty of Stimulus. It is claimed that children acquire certain
phenomena, which they cannot learn on the basis of experience alone ---and therefore,
there has to be some innate component for language. In this thesis, we show that at
least for certain phenomena that are often used in such arguments, it is possible to
explain how children acquire them on the basis of experience alone, viz. with an
Unsupervised Data-Oriented Parsing (U-DOP) approach to language.
In the first part of the thesis, we develop concrete implementations of U-DOP, and
contribute to the field of unsupervised parsing with two innovations. First, we develop
an algorithm that performs syntactic category labeling and parsing simultaneously, and
second, we devise a new methodology for unsupervised parsing, which can in principle
be applied to any unsupervised parsing algorithm, and which produces the best results
reported on the ATIS-corpus so far, with a promising outlook for even better results.
In the second part of the thesis, we then use these concrete implementations to show
how the acquisition of certain phenomena can be explained in an empirical way. We
look in detail at wh-questions, and then show that the U-DOP approach is more general
than the nativist account by looking at other phenomena.
Corresponding author: margauxsmets@gmail.com

PRESENTATION ABSTRACTS
Abstract
Age and Gender Prediction on Netlog Data
Peersman, Claudia 1,2 and Daelemans, Walter 1 and Van Vaerenbergh,
Leona 2
1 CLiPS, University of Antwerp
2 Artesis, University College Antwerp
In recent years millions of people have started using social networking sites such as
Netlog to support their personal and professional communications, creating digital
communities. However, a common characteristic of these digital communities is that
users can easily provide a false name, age, gender and location in order to hide their
true identity. This way, social networking sites can be used by people with criminal
intentions (e.g., paedophiles) to support their activities online.
In the context of the DAPHNE project (Defending Against Paedophiles in
Heterogeneous Network Environments), we present first results of a machine learning
approach for age and gender prediction on a corpus of posts on the social network site
Netlog. We investigate which types of linguistic and stylistic features are effective for
age and gender prediction, given the specific characteristics of (the Dutch) chat
language and compare the effectiveness of different machine learning techniques for
age and gender prediction on the Netlog data.
We will conclude our presentation by discussing how these results will guide future
research in the DAPHNE project.
Corresponding author: claudia.peersman@ua.ac.be
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26
CLIN 21 – CONFERENCE PROGRAMME
Aligning translation divergences through semantic role
projection
Abstract
Vanallemeersch, Tom
Centrum voor Computerlinguïstiek, K.U.Leuven
We investigate whether an alignment method based on cross-lingual semantic
annotation projection improves over approaches for linguistically uninformed word
alignment and purely syntax-based tree alignment, specifically in the area of
translation divergences. We apply an SRL system which annotates English sentences
with PropBank and NomBank rolesets (verbal and nominal predicates and their
semantic roles), and project the predicates and roles to Dutch and French using
intersective GIZA++ word alignment. We create additional alignment links by detecting
the auxiliary words of predicates (auxiliary, modal and support verbs) in parse trees
and by detecting potential Dutch or French predicates based on projected roles. Finally,
we investigate whether additional links can be created by training an SRL system on the
projected predicates and roles and applying it to the Dutch and French parse trees.
Corresponding author: tallem@ccl.kuleuven.be

PRESENTATION ABSTRACTS
Abstract
An Aboutness-based Dependency Parser for Dutch
Koster, Cornelis H.A.
Radboud Universiteit Nijmegen
Dupira (the Dutch Parser for IR Applications) is a new Dependency Parser for Dutch,
which was developed at the University of Nijmegen, based on the older Amazon
grammar and lexicon.
Dupira is a rule-based parser, which is generated by means of the AGFL parser
generator from the Dupira grammar, lexicon and fact tables. By means of transductions
which are specified in the grammar (and can be modified), the parser transduces
sentences to dependency trees.
Dupira was developed for applications in Information Retrieval (IR) rather than in
Linguistics, and for that reason has the following properties:
- the dependency model of Dupira expresses the aboutness of a sentence rather
than describing its complete syntactic structure;
- therefore it is highly suitable for extracting factoids from running text;
- it is also possible to extract dependency triples, which can be used as highaccuracy
terms for text categorization and full-text search;
- Dupira performs certain aboutness-preserving normalizing transformations,
including de-passivization and de-topicalization, in order to enhance recall;
- it makes extensive use of subcategorization preferences to resolve where
possible the attachment of Preposition Phrases;
- it is highly robust, both lexically and syntactically, and fast enough for practical
applications.
In this presentation, we discuss the aboutness-based dependency model and the way
in which the grammar describes the Dutch language. We show by means of examples
the transduction of clauses and phrases. We report the availability of Dupira Version
0.8 in the public domain and the plans we have for further development, and discuss
some of its applications.
Corresponding author: kees@cs.ru.nl
27

28
CLIN 21 – CONFERENCE PROGRAMME
An exploration of n-gram relationships for transliteration
identification
Abstract
Nabende, Peter
University of Groningen
Transliteration identification aims at building quality bilingual lexicons for
complementing and improving performance in various NLP applications including
Machine Translation (MT) and Cross Language Information Retrieval (CLIR). The main
task is to identify matching words across different writing systems from a given data
source. Recent evaluations (Kumaran et al., 2010) show that no single approach
achieves a consistently best performance in identifying transliterations from different
language pairs: an approach that leads to the identification of quality matches between
English and Russian, may result in many incorrect matches between English and
Chinese. In this paper, we conduct experimental settings of utilizing n-gram
relationships for computing candidate transliteration similarity scores which are
subsequently evaluated for choosing potential transliteration matches. We use
datasets from the 2010 transliteration generation shared task (Li et al., 2010) for five
language pairs: English-Russian, English-Chinese, English-Hindi, English-Tamil, and
English-Kannada. For each language pair, we explore various n-gram relationships
starting from the unigram case to higher order n-grams. Results show that higher order
n-grams lead to better transliteration identification quality across all languages,
however, for different language pairs, the higher order n-grams outperform each
other. For example, a pair trigram model outperforms a pair 4-gram model on an
English-Russian dataset while the reverse is true for an English-Pinyin(Romanized
Chinese) dataset. The results are promising as we aim at eliciting such n-gram
correspondences for use in more complex stochastic models such as pair Hidden
Markov Models (Pair HMMs) that we postulate may lead to even better transliteration
identification quality.
Corresponding author: p.nabende@rug.nl

PRESENTATION ABSTRACTS
Application of a Constraint Conditional Model for
improving the performance of a Sequence Tagger : A Case
Study
Abstract
Stokman, Tim
Textkernel
Natural language classifiers often ignore natural global constraints arising from the
nature of domain. These constraints are often hard to learn by the classifier because
the constraints are global while most classifiers make local decisions. Given that the
classifier can give a probability distribution on the possible assignments, we can search
this solution space for a set of assignments satisfying the natural constraints. Given this
solution space, we can formulate a ILP model that maximizes the probability of the
assignment while conforming to all constraints formulated in the model.
We apply this approach to a number of datasets and develop a set of natural
constraints for these datasets. We will expand on the practical implementation issues
encountered and show the performance improvements that arise from using the
constraint conditional model approach.
Corresponding author: timstokman@gmail.com
29

30
CLIN 21 – CONFERENCE PROGRAMME
Automatic terminology extraction: methods and practical
applications
Abstract
de Vries, Dennis
GridLine
Some of the usefull applications of terminology that GridLine provides are automatic
assignment of keywords to documents, integration of thesauri with search engines,
enrichment of search queries using a multi-lingual thesaurus and aiding writers in
correct use of terminology.
The problem that many organisations face though is that they don’t have a list of their
specific terminology. Creating these lists manually by examining company
documentation and interviewing experts is very expensive and time-consuming.
Therefore, GridLine developed instruments for automatically extracting organisation
specific terminology from document collections. Additionally, after extracting
terminology, we can build a thesaurus by automatically determining semantic relations
between terms.
For extraction of terms and semantic relations we use a variety of linguistic methods
(lemmatizer, POS-tagger, compound splitter) and statistical methods (unithood,
termhood). In this presentation I will give a brief overview of these extraction
techniques and show some examples of projects we did for our customers. In
particular, I will talk about Termtreffer, an easy to use application for term extraction
which we made for the Nederlandse Taalunie. In this application, users can extract
terms from documents using custom combinations of linguistic and statistical modules
for term extraction. Additionally they can manage, analyze and edit the resulting
terminology lists.
GridLine is a growing company based in the center of Amsterdam. Currently we are
market leader in Dutch language technology.
Corresponding author: dennis@gridline.nl

PRESENTATION ABSTRACTS
Abstract
Automatically Constructing a Wordnet for Dutch
Van de Cruys, Tim
INRIA & Université Paris VII
In this talk, we describe the automatic construction of a wordnet for Dutch by
combining a number of different sources of semantic information. First, a number of
unsupervised and semi-supervised techniques are presented for the extraction of
different kinds of semantic information. This includes techniques based on
distributional similarity and clustering, but also techniques that extract semantic
information from semi-structured and multilingual resources (such as Wiktionary). The
second part describes how the output of these techniques may be combined with the
structure of the original Princeton WordNet for English, which allows for the automatic
construction of a wordnet for Dutch. Contrary to existing resources, the extracted
resource also includes named entities. The resource is evaluated according to
CORNETTO, an existing, manually constructed wordnet for Dutch.
Corresponding author: Tim.Van_de_Cruys@inria.fr
31

Abstract
32
CLIN 21 – CONFERENCE PROGRAMME
Automatically determining phonetic distances
Wieling, Martijn and Margaretha, Eliza and Nerbonne, John
University of Groningen
This study seeks to induce the distance between phonetic segments based on their
correspondences in dialect atlas material. In other words, we induce information about
the physical realization of sounds from their dialectal distributions.
We algorithmically align segments in pairs of pronunciations at various sites in order to
identify corresponding sounds. We then apply an information-theoretic measure,
Pointwise Mutual Information, in order to automatically determine phonetic distances
based on the relative frequency of correspondences. We repeat these steps until the
alignments (and segment distances) stabilize.
We evaluate the quality of the obtained phonetic distances by comparing them to
acoustic vowel distances. For two separate dialect datasets, Dutch and German, we
find high significant correlations between the induced phonetic distances and the
acoustic distances, indicating that the frequency of correpondence in dialect material
conveys information about the constitution of sounds. We close with some
speculations about the usefulness of the method.
Corresponding author: m.b.wieling@rug.nl

PRESENTATION ABSTRACTS
Building a Gold Standard for Dutch Spelling Correction
Abstract
Gaustad, Tanja and van den Bosch, Antal
TiCC, Tilburg University
The main question in the NWO project "Implicit Linguistics" is whether abstract
linguistic representations are necessary as an intermediate step in NLP models and
systems. To investigate this, we focus on text-to-text processing tasks, i.e. processes
which map form to form. In particular, we are investigating Dutch spelling correction
where a corrupted text is converted to a clean version of the same text.
In order to test the quality of a spelling corrector, a Gold standard is needed. This,
however, does not exist for Dutch as of yet. For this reason, we set out to build such a
Gold standard, containing a mixed selection of texts in which we aim to mark all errors
and their corrections. In this talk, we will present the Gold standard including interannotator
agreement and other statistics relating to the data used. Furthermore, we
will present first results with applying our language model WOPR to the corpus,
comparing it against two baselines: a high-precision known error list and a contextinsensitive
lexical baseline. Evaluation is performed in terms of precision and recall on
detection and correction on full text.
Corresponding author: T.Gaustad@uvt.nl
33

Abstract
34
Nauze, Fabrice
Q-go
Clustering customer questions
CLIN 21 – CONFERENCE PROGRAMME
Q-go’s natural language search technology powers the search box of many corporate
websites. Its NLP technology allows customers to ask questions in their own words and
returns a small set of relevant answers. Hundreds of millions of questions have already
been processed and answered with Q-go’s solution providing us with a mine of data.
In order to improve our knowledge of what customers are asking and to help further
refine our core systems, Q-go needs a way to automatically cluster relevant queries
from large sets of customer questions.
To achieve this goal we tested several standard clustering methods on sets of customer
questions. The outline of the talk will be the following.
First we will explain the specific challenges one has to face when clustering customer
questions (very short queries, typos, etc…). We will then present the clustering
algorithms that have been tested (among other k-Means, GAAC hierarchical clustering,
mini-batch k-Means). Thirdly we will outline two different types of heuristics used in
the first case to improve the quality of the vector representations feeding the
clustering algorithms and in the second to overcome the curse of dimensionality.
Finally the different methods will be evaluated and compared with respect to
processing speed and intrinsic quality of clustering (as well as its practical usefulness).
Corresponding author: fabrice.nauze@q-go.com

PRESENTATION ABSTRACTS
Collecting and using a corpus of lyrics and their moods
Abstract
van Zaanen, Menno
Tilburg University
Recently, there has been an increase in availability of music in digital formats. This has
led to music collections that are different in nature than in the past. Collections are
typically larger and consist of a selection of individual pieces instead of complete
albums. Since playing any musical piece from the collection can be done without
physically changing the medium, listeners create playlists that allow them to identify a
subset of the collection and determine the order in which the pieces are played.
People creating playlists often want to group pieces based on their emotional load
(such as happy or sad). Creating such playlists, however, is time-consuming and
requires knowledge of the music in the collection, since emotional information is not
explicitly encoded with the pieces. We will describe a system that analyzes musical
pieces and, based on the lyrics, classifies them into their corresponding mood class.
This system is developed and evaluated using a corpus of lyrics of songs and their
corresponding mood. The mood tags were collected by social tagging of musical pieces
using the Moody iTunes plugin that is developed by the company Earth People within
the Crayon Room project. Starting from a list of artist, title and mood triples, the
corresponding lyrics of the songs have been collected. This has led to a corpus
containing the lyrics of 5,631 songs, which will be made publicly available.
Corresponding author: mvzaanen@uvt.nl
35

36
CLIN 21 – CONFERENCE PROGRAMME
Combined Qualitative and Quantitative Error Analysis in
Multi-Topic Authorship Attribution
Abstract
Luyckx, Kim and Daelemans, Walter
CLiPS, Antwerp University
In authorship attribution, function words are considered the ideal feature type to deal
with the complexity of the task. There is a consensus that they are topic-neutral, highly
frequent, and not under the author's conscious control. However, it has been shown
that hardly any of these allegedly topic-neutral features are in fact topic-neutral. Topic
seems to be hard to 'separate' from authorial style, irrespective of the type of features
used to predict authorship. Although function words are robust to limited data and
provide good indicators of authorship, the a-priori exclusion of content words causes a
lot of useful information to be disregarded.
We discuss experiments in multi-topic authorship attribution and zoom in on the
features that constitute the attribution model. Qualitative analysis of results is typically
lacking in authorship attribution studies, since many studies focus on performance, but
refrain from going into detail about the features selected.
In this talk, we show that high performance does not always imply a viable approach.
More specifically, we zoom in on unique identifiers, features that occur exclusively with
a specific authorship class in training and uniquely identify a test instance by the same
author. Although a coincidence - the frequency of a feature in an unseen test set or the
topic of that test set cannot be predicted - topic-related unique identifiers provide the
model with an unfair advantage that will not scale. However, the absence of unique
identifiers does not necessarily imply a scalable approach. Although this talk focuses on
authorship attribution, we think any task in text mining would benefit from consistent
error analysis.
Corresponding author: kim.luyckx@ua.ac.be

PRESENTATION ABSTRACTS
Combining e-learning and natural language processing.
The example of automated dictation exercises
Abstract
Beaufort, Richard and Roekhaut, Sophie
UCL CENTAL
E-learning is a way of delivering education based on the use of electronic tools and
content, either delivered on CD-ROMs or managed through network connections. The
idea behind e-learning is to improve both the learning and its management. To this
end, exercises are frequently automated. Of course, the ideal automation would
involve the three distinct steps of an exercise: its preparation, its realization (by the
student) and its correction.
Up to now, the automation lead to exercises, like gap-fill texts or multiple choice tests,
which limit the kinds of knowledge that can be assessed. This is due to the fact that the
correction step, for some exercises, is far from easy to automate.
An eloquent example of such an exercise is dictation, this activity where the teacher
reads a passage aloud and the learners write it down. While automatically reading an
unknown text aloud is not a problem as long as a reliable text-to-speech synthesis
system is available, the accurate correction of a learner's copy can quickly become a
nightmare.
The correction of a dictation's copy involves two steps: first, the detection of the real
places of errors: second, the classification of these errors. In this paper, we present a
way of automating these two steps. The detection step is based on a finite-state string
alignment between the copy and the original. The classification step is the best result
of a finite-state intersection between all possible automatic analyses of an error and
the single analysis of the corresponding correct form.
Corresponding author: richard.beaufort@uclouvain.be
37

Abstract
38
CLIN 21 – CONFERENCE PROGRAMME
Computing Semantic Relations from Heterogeneous
Information Sources
Panchenko, Alexander
UCL CENTAL
Computation of semantic relations between terms or concepts is a general problem in
Natural Language Processing and a subtask of automatic thesaurus construction.
This work describes and compares available heterogeneous information sources which
can be used for mining semantic relations such as texts, electronic dictionaries and
encyclopedias, lexical ontologies and thesauri, folksonomies, surfaces of words, query
logs of search engines, and so forth. Most of the existing algorithms use a single
information source for extracting semantic knowledge: Distributional Analysis relies on
text, Extented Lesk uses dictionary definitions, Jiang-Conrath distance employs a
semantic network such as WordNet and so on. We show that different methods
capture different aspects of the terms’ relatedness: while one acquires similarities of
word contexts, others capture similarities of syntactic contexts, term definitions,
surfaces forms etc.
In these settings, there is a need for a general model capable to aggregate different
aspects of semantic similarity from all available information sources and methods in an
optimal and consistent way. We discuss how such a model can be implemented with a
linear combination, and using tensors (i.e. multi-way arrays). We describe two ways of
using tensors for calculation of semantic relations in the context of multiple
information sources, which we call “adjacency tensor” and “feature tensor”. The sparse
tensor factorization methods PARAFAC, Non-negative Tensor Factorization (NTF), and
Memory-Efficient Tucker (MET) are suggested in order to fusion information about
terms from different methods and information sources. We conclude that tensors can
be used for representing terms, while tensor factorizations can serve to generalize data
about terms’ relatedness.
Corresponding author: alexander.panchenko@student.uclouvain.be

PRESENTATION ABSTRACTS
Computing the meaning of multi-word expressions for
semantic inference
Abstract
Cremers, Crit
Leiden University
The immense diversity of multi words expressions in every language imposes heavy
requirements on the lexicon, the grammar and their interface for deep semantic
analysis to be feasible. The lexicon for meaning-driven NLP is huge and phrasal.
We present a model for dealing with extended lexical units in a parser/generator for
Dutch that aims at the logical computation of entailments and presuppositions. The
model consists of three components: a fiat architecture for the computational lexicon,
an efficient organization of on-line lexical retrieval and a selectional method of
semantic underspecification. In the fiat lexicon, all combinatory instances of all lexical
varieties of all (semantically) relevant constructions are spelled out as feature-value
graphs. Each of the feature-value graphs contains all combinatory information needed
for synatctic and semantic processing. This constructicon is produced off line. It is
managed on line by a retrieval system that selects contextually required and adequate
constructions in linear time. The underspecification allows to disambiguate the
combinatory result by evaluating the structure of the representations.
The model will be demonstrated by reference to two particular constructions: the
Dutch way- (Poss 2009) and the Dutch honger-construction. The first one – “jij hebt je
een weg uit de gevangenis geslijmd” - exemplifies an intriguing combination of lexical
restrictions, productivity, structure sensitivity and semantic specificity. The second – “ik
heb geen erg grote honger” - exemplifies transcategorial semantic effects, where open
modification of a noun phrase requires translation into propositional and statesensitive
operators.
Corresponding author: c.l.j.m.cremers@hum.leidenuniv.nl
39

Abstract
40
CLIN 21 – CONFERENCE PROGRAMME
Cross-Domain Dutch Coreference Resolution
De Clercq, Orphée and Hoste, Véronique
LT3 Language and Translation Technology Team, University College
Ghent
For the STEVIN funded SoNaR project, a Dutch reference corpus of 500 million words is
being built. At the same time, a one-million-word subset is progressively enriched with
semantic information: named entities, coreference, spatio-temporal relations and
semantic roles. As a prerequisite, existing schemes and systems developed for Dutch
are to be reused to the fullest extent possible. In this talk we present the ongoing task
of annotating this subset with coreference information, following existing guidelines
for Dutch (Bouma et al. 2007).
The basis for our coreference resolver is an existing mention-pair approach (Hoste
2005, Hendrickx et al. 2008) for Dutch. One of the main challenges in the domain of
coreference resolution is portability across different domains and languages. Since one
of the great advantages of the SoNaR corpus is its diversity - the 1MW subset itself
comprises six text types - we decided to train our system on, respectively, each text
type separately and all types combined. We will report cross-type (e.g. administrative,
external communication, instructive, journalistic text) cross-validation results for
different NP types and present an extensive qualitative error analysis. We compare
performance when providing perfect markables, derived from deep parsing (Alpino,
Van Noord et al. 2006) with automatically generated markables, and investigate the
added value of integrating additional semantic information resulting from other
annotation layers.
References
G. Bouma, W. Daelemans, I. Hendrickx, V. Hoste, and A. Mineur. 2007. The COREAproject,
Manual for the annotation of coreference in Dutch texts. Technical report,
University Groningen.
I. Hendrickx, V Hoste, and W. Daelemans. 2008. Semantic and Syntactic features for
Anaphora Resolution for Dutch. In Lecture Notes in Computer Science, Volume 4919,
Proceedings of the CICLing-2008 conference, pages 351–361. Berlin: Springer Verlag.

PRESENTATION ABSTRACTS
V. Hoste. 2005. Optimization Issues in Machine Learning of Coreference Resolution.
Ph.D. thesis, Antwerp University.
G. Van Noord, I. Schuurman, and V. Vandeghinste. 2006. Syntactic Annotation of Large
Corpora in STEVIN. In Proceedings of LREC 2006, Genua.
Corresponding author: orphee.declercq@hogent.be
41

42
CLIN 21 – CONFERENCE PROGRAMME
Dmesure: a readability platform for French as a foreign
language
Abstract
François, Thomas
UCL CENTAL
It is a well-known fact that reading practice improves the reading abilities of L1
students as well as L2 students. However, once one strays from textbooks, matching
individuals with texts of an adequate level of difficulty is far from an easy task. FFL
teachers have all, at some time or other, wasted time carrying out such a task.
Our research aims at providing to the community a web platform, called Dmesure, able
to retrieve from the web texts on a specific topic and at a specific readability level. We
will present the current version of this platform, in which texts are first retrieved
through the Yahoo search engine, before being assessed for difficulty using the
readability measure described in Francois (2009). It is worth noting that the output of
Dmesure is compliant with the proficiency scale set in the Common European
Framework of Reference for languages, what makes this tool very convenient for FFL
teachers.
We also address some specific problems encountered when applying a readability
measure to web texts. Among them, we consider the influence of the boilerplate on
the readability measure, and some ways to reject pages whose language diverge too
much from the norm. To conclude, we show that because Dmesure has been
developped in a participative perspective, it allows to collect new texts annotated by
teachers. Therefore, the build in readability model can be retrain occasionally with this
enhanced corpus.
Corresponding author: thomas.francois@uclouvain.be

PRESENTATION ABSTRACTS
Abstract
Essentials of person names
Schraagen, Marijn
Leiden Institute of Advanced Computer Science
The frequency of spelling variation and errors in person names is relatively high,
compared to normal vocabulary. Standardization of a name to some base form, or
core, could be useful in named entity matching or record linkage. Two types of core are
investigated: the semantic core and the syntactic core. The semantic core approach
exploits the idea that names in Dutch, especially surnames, have meaning: a surname is
usually based on a first name, a location, a profession or a personal characteristic. The
semantic component is subject to heavy and unpredictable modifications due to
suffixes, inflections and compounding, therefore suffix removal techniques are less
successful for names than for standard vocabulary. The semantic component itself is
however relatively stable, and the set of semantic categories is reasonably restricted.
Therefore, a word list approach can be applied for names, which avoids learning or
designing complex suffix removal rules. An alternative approach is to extract the
syntactic core of a name. The syntactic core is the (possibly discontinuous) character
sequence that remains constant or phonetically equivalent in all variants of a name.
The syntactic core can be analysed on various linguistic levels. An advantage of the
syntactic approach is that a word list is not needed, and therefore the procedure can
also be applied to unknown names or names without a vocabulary-based meaning
component (such as first names). Algorithms for extracting semantic and syntactic
cores are discussed, and an application is provided for the problem of record linkage in
data mining.
Corresponding author: schraage@liacs.nl
43

44
CLIN 21 – CONFERENCE PROGRAMME
Extraction of Historical Events from Unstructered Texts
Abstract
Segers, Roxane and van Erp, Marieke and van der Meij, Lourens
VU University Amsterdam
Historiography revolves around events as these express important changepoints in
reality. We postulate that events can play an important role in improving automated
search and data integration in the historic domain as events connect information about
who did what where and when.
We present a pattern based approach to automatically extract historical named events
like "French Revolution" and "Second World War" from unstructured texts in Dutch.
The extracted events are the backbone of a structured event thesaurus that will consist
of events with their time, place and participants.
In our approach we make a distinction between external and internal event patterns.
For collecting external event patterns like 'during the', we retrieved text snippets for a
number of seed events. We ranked the pattern candidates by their frequency and cooccurence
with different events. Next, we ran the pattern collection over a domain
specific corpus. We evaluated the precision of the extracted historical event candidates
by the number of patterns that extracted the event and the confidence score of these
patterns.
The extracted events were used as input for obtaining event internal patterns. We
classified and analysed the events based on their morpho-syntactic structure: this
yielded patterns such as "Massacre of Y". To expand these patterns, we used the head
of the events to iterate Wordnet: this yielded new internal patterns such as "Bloodbath
of Y".
As a result we obtained a library of external and internal patterns that can be used to
extract named events from unstructured texts. The presented combination of internal
and external patterns is vital as our combined library outperforms each pattern type on
its own.
Corresponding author: rh.segers@vu.nl

PRESENTATION ABSTRACTS
Abstract
Finding Statistically Motivated Features Influencing
Subtree Alignment Performance
Kotzé, Gideon
University of Groningen
We present results of an ongoing investigation of a manually aligned parallel treebank
and an automatic tree aligner. Using the parallel treebank as a test set, features that
are shown to have a significant correlation with alignment performance are
established. Our conclusion is that lexical features generally have a more significant
influence than tree features. We present these findings with a discussion of their
significance and with reference to possible useful applications in the alignment of
parallel texts for machine translation.
Corresponding author: g.j.kotze@rug.nl
45

Abstract
46
CLIN 21 – CONFERENCE PROGRAMME
From Tokens to Text Entities: Line-based Parsing of
Resumes using Conditional Random Fields
Rotaru, Mihai
Textkernel NL
Resumes (Curriculum Vitae) form a challenging category of semi-structured
documents. Regardless of the language, most resumes tend to be structured in
sections: e.g. experience, education, skills, personal. Consequently, the first task of a
resume information extraction system is to segment the resume in sections. We cast
the section segmentation problem as a sequence labeling problem. In this paper, we
show practical results that compare two approaches. The first approach works at the
word level and uses Hidden Markov Models (HMM) with words as the HMM
observations. The second approach works at the line level and uses Conditional
Random Fields (CFRF) and a variety of features computed for each line. We find that
the CRF approach outperforms the HMM approach significantly on this real world task,
and that the improvement is also reflected in the later stages of our resume
information extraction pipeline. In addition, this result generalizes across several
languages after porting the corresponding CRF features. The main advantages of the
CRF approach are the expressiveness of the features (e.g. easily express information
that spans multiple words), and the fact that it makes the practical assumption that a
line of text belongs to a single section.
Corresponding author: rotaru@textkernel.nl

PRESENTATION ABSTRACTS
Im chattin :-) u wanna NLP it: Analyzing Reduction in Chat
Abstract
van Halteren, Hans 1 and Martell, Craig 2 and Du, Caixia 3 and Gu, Yan 3
and Johan, Kobben 3 and Panjaitan, Leequisach 3 and Schubotz, Louise 3
Vasylenko, Kateryna 4
1 Radboud University Nijmegen
2 Naval Postgraduate School, Monterey
3 ReMa L&C, RUN/UvT
4 ReMa L&C
Modern NLP research attempts to cover the whole spectrum from written to spoken
text. Right in the middle we find chat text, a written text type which has many
similarities with spoken text. One of these is spelling variation, often reduction, e.g.
nite instead of night. It is clear that, if we ever want to analyze or generate chat text,
we have to understand the factors behind this spelling behavior, whether user
experience with SMS, peer group identification by speech spelling or otherwise.
This paper contributes by studying spelling reduction in chat text. We investigated
cases of reduction in the NPS Chat Corpus. After identifying various types in 2000 posts
from the publicly available part of the corpus, we focused on four frequent
phenomena: a) wanna (want to) and gonna (going to), b) ya and u (you), c) g-drop in
present participles, e.g. findin for finding d) apostrophe drop in enclitics, e.g. hes for
he’s. For these, we automatically extracted all occurrences of both reduced and full
forms in 1Mw from the complete corpus. For each, we also determined features which
could be of influence on the choice between the alternating forms, such as the poster’s
age group and immediate context in the post. On the basis of this, we built regression
models to find out which of the features show a significant influence.
In the paper, we present the main findings and relate them to those identified in the
literature as being active in spoken text.
Corresponding author: hvh@let.ru.nl
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48
CLIN 21 – CONFERENCE PROGRAMME
Language Evolution and SA-OT: The case of sentential
negation
Abstract
Lopopolo, Alessandro and Biro, Tamas
University of Amsterdam
Simulated Annealing Optimality Theory (SA-OT) is a recent update of the OT
framework, and it adds a model of performance to a theory of linguistic competence.
Our aim is to show how SA-OT can be useful for Language Evolution simulations.
Performance error is a central concept in this model, and it is considered to be one of
the causes of variation and evolution. In performance, speakers accept sacrificing
precision in order to enhance communicative strength, and the performance errors
influence the language learning of the next generation.
In order to test the potentialities of SA-OT, we have chosen to model the evolution of
sentential negation. The background is based on Jespersen's Cycle (JC). In JC, the
evolution of sentential negation follows three stages (1. pre-verbal, 2. discontinuous,
and 3. post-verbal). Our starting point is the treatment of JC by de Swart (2010) in
terms of traditional OT. Her model predicts six stages: the three above-mentioned pure
stages, as well as three intermediate, mixed stages. Yet, there are no convincing
empirical data for an intermediate stage between stages 1 and 3.
Therefore, we advance a novel, computational model for JC, based on SA-OT. It
reproduces the three pure and the two observed mixed stages, whereas it correctly
predicts the lack of an intermediate stage between 1 and 3. This result makes different
predictions for the evolution of sentential negation, and confirms the validity of SA-OT
as a computational model for language evolution.
Corresponding author: A.Lopopolo@student.uva.nl

PRESENTATION ABSTRACTS
Machine Learning Approaches to Sentiment Analysis
Using the Dutch Netlog Corpus
Abstract
Schrauwen, Sarah and Daelemans, Walter
CLiPS, Antwerp University
Sentiment analysis deals with the computational treatment of opinion, sentiment and
subjectivity. We constructed and manually annotated a corpus, the Dutch Netlog
Corpus, with data extracted from the social networking website Netlog. This corpus
was annotated on three levels: ‘valence’ (expressing the opinion of the writer: we
distinguish between ‘positive’, ‘negative’, ‘both’, ‘neutral’ and ‘n/a’) and additionally
language performance, which is divided into two areas: ‘performance’ (‘standard’,
‘dialect’ and ‘n/a’) and ‘chat’ (‘chat’, ‘non-chat’ and ‘n/a’). We tackle sentiment analysis
as a text classification task and employ two simple feature sets (the most frequent and
the most informative words of the corpus) and three supervised classifiers
implemented from the Natural Language ToolKit (the Naïve Bayes, Maximum Entropy
and Decision Tree classifiers). The highest obtained accuracy score for valence
classification with the entire data set is a 65.1%.
We suggest three factors leading to errors in valence classification. First, the nature of
the data affects results, since most of the corpus is made up of dialect and chat
language, which is more difficult to predict. Second, the number of classes to predict
from is larger for valence classification (five classes) than for performance or chat
classification (three classes), and is therefore also more difficult to process. Third, the
skewed class distribution of the corpus probably has the biggest influence on the
results. We suspect that more training data will solve these current problems.
Corresponding author: sarah.schrauwen@gmail.com
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Measuring the Impact of Controlled Language on Machine
Translation Text via Readability and Comprehensibility
Abstract
Doherty, Stephen
Centre for Next Generation Localisation, Dublin
This paper describes a recent study of the readability and comprehensibility of English
software documentation, which has been translated into French by Matrex, a state-ofthe-art
(phrase-based statistical) machine translation system. The primary aim of the
study is to examine what, if any, effects there are on the readability and
comprehensibility of the machine translation output following the application of
controlled language (CL) rules on the source language texts. Our hypothesis is that the
application of CL rules would result in an observable increase in readability and
comprehensibility of the target language text.
Our approach consisted of a three-pronged evaluation of the texts by means of (i)
readability indices in both the source and target languages: (ii) an eye tracking
measurement of readability: and (iii) a post-task qualitative measurement of
comprehensibility, using recall and likert-scale human evaluations. We also looked at
correlations between automatic machine translation evaluation metrics (e.g. BLEU,
GTM etc.) and the evaluation results mentioned above in an attempt to bridge the gap
between human and automatic approaches to evaluation.
The paper will first describe some background and context in the relevant research
areas, followed by a presentation of the methods employed with a particular focus on
the measurement of readability via eye tracking and tentative results in this regard.
Corresponding author: stephen.doherty2@mail.dcu.ie

PRESENTATION ABSTRACTS
Abstract
Memory-based text completion
van den Bosch, Antal
Tilburg University
The commonly accepted technology for fast and efficient word completion is the prefix
tree, or trie. As a word is keyed in, the trie can be queried for unicity points and best
guesses. We present three improvements over the normal prefix trie in experiments in
which we measure the percentage of keypresses saved on both in-domain and out-ofdomain
test text, emulating a perfectly alert user who would select correct suggestions
promptly. First, we train a suffix trie that tests backwards from the most recent
keypresses. Conditioned on first letters, the suffix trie model yields about 10% more
saved keypresses than the baseline character saving percentage on in-domain test
data. Second, the suffix trie model can be straightforwardly extended to testing on
characters of previous words. Adding this context yields another 10% increase in
character savings. Third, when we train the context-rich suffix trie model to complete
the current word and predict the next one in one go, character savings go up another
4%. In a learning experiment on Dutch texts we observe character savings of up to 44%
on in-domain test data where the baseline prefix tree savings percentage is 19%. On
out-of-domain twitter data, the prefix trie baseline of 19% is only mildly surpassed by
the suffix tree variants to 24% character savings. We develop an explanation for the
spectacular success of the suffix tree approach on in-domain data, and review the
applicability of the approach in real-world text entry contexts.
Corresponding author: Antal.vdnBosch@uvt.nl
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Abstract
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CLIN 21 – CONFERENCE PROGRAMME
Overlap-based Phrase Alignment for Language
Transformation
Wubben, Sander and van den Bosch, Antal and Krahmer, Emiel
Tilburg University
In this talk we will present our work on the task of paraphrasing from an old variant of
a language to a modern variant. One of the tasks we consider is paraphrasing the
Canterbury Tales from Middle English to Modern English. We approach this task as a
translation task and therefore we use Machine Translation techniques. The current
state of the art Machine Translation systems rely heavily on statistical word alignment.
The alignment package most commonly used is GIZA++, which is used to train IBM
Models 1 to 5 and an HMM word alignment model. The benefit of using statistical word
alignment is that no assumptions need to be made about the parallel corpus and that it
generally produces better results when being fed more data. This holds for the task of
paraphrasing as well. However, when we consider monolingual parallel corpora, it
might be naive to only use statistics when we can in fact utilize the attribute that both
sides of the corpus are in the same (or at least similar) language, and therefore likely to
exhibit a certain amount of overlap. We will investigate the feasiblity of using overlap
measures to align words and phrases in monolingual corpora and how this method
holds up against pure statistical alignment in a Machine Translation framework.
Corresponding author: s.wubben@uvt.nl

PRESENTATION ABSTRACTS
Abstract
Parse and Tag Somali Pirates
van Erp, Marieke and Malaisé, Véronique and van Hage, Willem and
Osinga, Vincent and Coleto, Juan Manuel
VU University Amsterdam
Events are the most prevalent complex entities described in user contributed social
network activities, newswire, commercial infringement reports etc. Unfortunately, due
to the nature of free text, event descriptions can take many forms, making querying for
or reasoning over them difficult.
We present an approach for event extraction from piracy attack reports issued by the
International Chamber of Commerce (ICC-CCS[1]). As the piracy attack reports are
semi-structured, we can treat the extraction task as a segmentation and labelling
problem. We extract information from the reports about participants, weapons,
locations, times and types of events, and store the information as structured event
instances. We argue that an event model is not only an intuitive representation for
such information, enabling automatic analysis and reasoning over the attacks and their
components, but also a very powerful tool for knowledge and data integration. We
show that the event model enables automatic analysis of the data, so questions such as
"How did the weapon use of pirates evolve over time?" can be answered.
[1] http://www.icc-ccs.org
Corresponding author: marieke@cs.vu.nl
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Personalized Knowledge Discovery: Combining Social
Media and Domain Ontologies
Abstract
Markus, Thomas and Westerhout, Eline and Monachesi, Paola
Utrecht University
We present a system that facilitates knowledge discovery by means of structured
domain ontologies. The user can discover new concepts and relations by exploring an
expert approved ontological structure which has been automatically enriched with new
concepts, relations and lexicalisations originating from social media. The system also
on-the-fly interlinks the conceptual knowledge in the ontology with noisy data coming
from social media on the conceptual level.
Our ontology enrichment methodology identifies salient terms using similarity
measures and determines the appropriate word senses for each term by employing a
disambiguation algorithm. The appropriate relation between the new concept (word
sense) and the existing ones is either extracted from DBpedia or from text documents
retrieved from the web. The disambiguation algorithm is also used to store the original
context of each term, that is, the term itself, its meaning, associated person and
resource. These personalised contexts are stored using the MOAT semantic vocabulary.
The enriched ontology and the disambiguation methodology allow us to give a
personalised semantic interpretation to each search result in the context of the
enriched domain ontology and the user. The amount of conceptual overlap between a
document and the person using the system is employed to offer personalised
recommendation of documents.
The advantages that this approach brings to students has been evaluated as part of a
university course with a large group of students and a separate control group.
Corresponding author: Thomas.Markus@phil.uu.nl

PRESENTATION ABSTRACTS
Recent Advances in Memory-Based Machine Translation
Abstract
van Gompel, Maarten and van den Bosch, Antal and Berck, Peter
Tilburg University
We present advances in research on Memory-based Machine Translation (MBMT), a
form of machine translation in which the translation model takes the form of
approximate k-nearest neighbour classifiers. These classifiers are trained to map words
or phrases in context to a target word or phrase. The modelling of source-side context
is a key feature distinguishing this approach from standard Statistical Machine
Translation (SMT).
In 2010 we released the open source PBMBMT (phrase-based memory-based machine
translation) system. PBMBMT embraces the concept of phrases, as opposed to the
single words or fixed n-grams that earlier work in memory-based machine translation
focused on. PBMBMT employs a phrase translation table generated by Moses as the
basis for the generation of training and test instances for our classifiers. We present an
automatic method for hyperparameter optimisation, and investigate the usage of
example weighting in the memory-based classifier. We critically measure and compare
the performance of our latest system against its precursor systems, and a state-of-theart
competitor.
A recent branch of research has focused on the language model component of
PBMBMT. As PBMBMT can work with both the well known SRILM software and WOPR,
the memory-based language model, we performed a learning curve experiment with
both language models to investigate the effect of the amount of training data. Our
results challenge the common "more data is better" belief.
Corresponding author: proycon@anaproy.nl
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Abstract
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Reversible stochastic attribute-value grammars
de Kok, Daniël and van Noord, Gertjan and Plank, Barbara
University of Groningen
Attribute-value grammars have been advocated because they are reversible. Their
declarative nature ensures that the same grammar can in principle be used for parsing
and generation.
In more recent years, attribute-value grammars have been extended with conditional
models to perform parse disambiguation and fluency ranking. However, since such
models are conditioned on a sentence or a logical form, reversibility is sacrificed.
We propose a framework for reversible stochastic attribute-value grammars. In this
framework, a single statistical model is used for parse disambiguation and fluency
ranking. We argue that this framework is more appropriate, since it recognizes that
preferences are shared between production and comprehension components. For
instance, if fluency ranking and disambiguation would have different preferences with
respect to subject fronting in Dutch, communication would become problematic.
We provide experimental results that show that the performance of a reversible model
does not differ significantly from directional models for parse disambiguation and
fluency ranking. We also show that fluency ranking models can be improved by adding
annotated parse disambiguation training data, and vise versa.
Corresponding author: d.j.a.de.kok@rug.nl

PRESENTATION ABSTRACTS
Abstract
Robust Rhymes? The Stability of Authorial Style in
Medieval Narratives
Kestemont, Mike and Daelemans, Walter and Sandra, Dominiek
CLiPS , University of Antwerp
We explore the application of stylometric methods developed for modern texts to
rhymed medieval narratives (Jacob of Maerlant and Lodewijk of Velthem, ca. 1260-
1330). Because of the peculiarities of medieval text transmission, we propose to use
highly frequent rhyme words for authorship attribution. First, we shall demonstrate
that these offer important benefits, being relatively content-independent and wellspread
over texts. Subsequent experimentation shows that correspondence analyses
can indeed detect authorial differences using highly frequent rhyme words. Finally, we
demonstrate for Maerlant’s oeuvre that these highly frequent rhyme words’ stylistic
stability should not be exaggerated since their distribution significantly correlates with
the internal structure of that oeuvre.
Corresponding author: mike.kestemont@ua.ac.be
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Abstract
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Rule Induction for Synchronous Tree-Substitution
Grammars in Machine Translation
Vandeghinste, Vincent and Martens, Scott
Centrum voor Computerlinguïstiek - KULeuven
Data-driven machine translation systems are evolving from string-based systems
towards tree-based systems, such as the PaCo-MT system. In this system, the source
language sentence is parsed using a monolingual parser. This parse tree needs to be
converted or transduced into one or more target language parse trees from which one
or more target language sentences can be generated.
Rules are induced from phrasal alignments in an automatically parsed version of the
English and Dutch portions of the Europarl treebank. The procedure for extraction
assumes that the subtrees bounded by alignments between phrasal nodes in the two
syntactic tree-structures are suitable as rules for a Synchronous Tree Substitution
Grammar. The maximum number of such trees are extracted, given the alignments
between the two sentences, and collected over the entire corpus. Rules that occur
multiple times are inserted into the transducer as tree substitution rules. Minimally
small tree substitution rules -- those consisting of a single node and its parent -- are
used to induce translations where the extracted rules have insufficient coverage.
Corresponding author: vincent@ccl.kuleuven.be

PRESENTATION ABSTRACTS
Abstract
Search in the Lassy Small Corpus
van Noord, Gertjan and de Kok, Daniel and van der Linde, Jelmer
University of Groningen
A few months ago, the STEVIN Lassy project yielded its most important results: Lassy
Small - a corpus of 1 million words with syntactic annotations which have been
manually verified and corrected, and Lassy Large - a corpus of 1.5 billion words with
automatically assigned syntactic structures. Syntactic annotations include part-ofspeech
tags, lemma and dependency annotations of the type developed earlier in CGN
and D-Coi.
In this presentation we focus on the Lassy Small corpus, and introduce a stand-alone
portable tool called DACT which can be used to browse the syntactic annotations in an
attractive graphical form, and to search for sentences according to a number of search
criteria, which can be specified elegantly by means of search queries formulated in
XPATH, the WWW standard query language for XML documents. We provide a number
of linguistically relevant examples of such queries, and we review the criticism of Lai
and Bird (2010) which they take as motivation to introduce LPATH, an extension of
XPATH. We will argue that such an extension is not required if string positions are
explicitly encoded as XML attributes, as is the case in Lassy Small.
DACT is freely available for various platforms, including Mac OS and recent versions of
Windows.
Corresponding author: g.j.m.van.noord@rug.nl
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CLIN 21 – CONFERENCE PROGRAMME
Simple Measures of Domain Similarity for Parsing
Plank, Barbara and van Noord, Gertjan
University of Groningen
It is well known that parsing accuracy suffers when a model is applied to out-of-domain
data. It is also known that the most beneficial data to parse a given domain is data that
matches the domain (Sekine 1997, Gildea 2001). Hence, an important task is to select
appropriate domains. However, most previous work on domain adaptation relied on
the implicit assumption that domains are somehow given.
With the growth of the web, more and more data is becoming available, and automatic
ways to select data that is beneficial for a new (unknown) target domain are becoming
attractive. We consider various ways to automatically acquire related training data for
a given test article, and compare automatic measures to human-annotated meta-data.
The results show that a very simple measure of similarity based on word frequencies
works surprisingly well.
Corresponding author: b.plank@rug.nl

PRESENTATION ABSTRACTS
Abstract
SSLD: A smart tool for sms compression
Cougnon, Louise-Amélie and Beaufort, Richard
UCLouvain - IL&C - Cental
Since 2009, we have been designing a methodology to semi-automatically develop a
dictionary based on a corpus of SMS. Such a dictionary can be used to help systems
translate from standard into sms language, a procedure which has so far been seen as
a entertaining activity: our methodology can also be employed for more serious
purposes such as text message summarising and compression tools. Our first results
were encouraging (Cougnon and Beaufort, 2010) but only focused on French data
(from Belgium, Quebec, Switzerland and La Reunion). Thanks to the sms4science
project that aims at collecting sms corpora from all over the world, we now have at our
disposal German, Dutch and Italian text messages. The aim of this paper is to describe
our three-step approach to the extraction of the dictionary entries from the various
corpora and to detail the smart manual sorting performed on the dictionary. The
results will give us the opportunity to test our initially French-based methodology on
other languages and to find out whether our approach is generic, i.e. applicable to all
languages. This question also paves the way for a panorama of sms phenomena
observed in the dictionary and which occur throughout the languages. Finally, we
propose ways in which our methodology could be further improved.
Corresponding author: louise-amelie.cougnon@uclouvain.be
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Subtrees as a new type of context in Word Space Models
Abstract
Smets, Margaux and Speelman, Dirk and Geeraerts, Dirk
QLVL, K.U.Leuven
In Word Space Models (WSMs) there are traditionally two types of contexts that can be
used: (i) lexical co-occurrences (`bag-of-words models') and (ii) syntactic dependencies.
In general, models with the second type of contexts seem to perform better. However,
there are some problems with these models. In the first place, a choice has to be made
which contexts to include: only subject/verb and verb/object-relations, or also other
dependencies . Second, in contrast with bag-of-words models, the syntactic models are
supervised: they require quite large resources (a dependency parser, a manually
annotated corpus, . . .), which might not be available for each language .
The contexts we propose for use in WSMs are subtrees as defined in the framework of
Data-Oriented-Parsing. Subtrees can capture both bag-of-words (co- occurrence)
information, and syntactic information. Moreover, they are not limited to specific types
of dependencies, but rather take entire structures into account.
At first sight, it might seem that the problem of resources for dependency-WSMs
remains in this framework. After all, we first need the `correct' tree for a sentence,
before we can extract subtrees from it. However, in our experiments we show how the
entire algorithm can be made unsupervised by using an unsupervised parser as a
preprocessing step.
In the presentation, I will first discuss in detail the workings of this new type of WSM.
Next, I will present some initial results from experiments with parameters such as the
accuracy of the parser in the preprocessing step, the maximum subtree depth, the
minimum subtree frequency, and considering only subtrees with the highest variance.
Corresponding author: margauxsmets@gmail.com

PRESENTATION ABSTRACTS
Successful extraction of opposites by means of textual
patterns with part-of-speech information only.
Abstract
Lobanova, Anna
Department of Artificial Intelligence, University of Groningen
We present an automatic method for extraction of opposites (e.g., rich - poor, top -
bottom, buy - sell) by means of textual patterns that only contain part-of-speech
information about target word pairs, e.g., difference between and . Our
preliminary results suggest that this method outperforms a pattern-based method that
uses dependency patterns [2] (requiring more sophisticated data preprocessing),
especially for opposites expressed by nouns and verbs.
Starting with small seed sets, we automatically acquired textual patterns from a 450
million word version of Twente Nieuws Corpus of Dutch [4]. All patterns were
automatically evaluated based on their overall frequency and the number of times they
contained seed pairs. Best patterns were used to find candidate pairs. All found pairs
were automatically scored based on their frequency and co-occurrence in reliable
patterns. In addition, pairs with the highest scoring =0.9 were evaluated by two human
judges. The precision scores for the top-100 found pairs were 0.61 for adjectiveadjective
pairs, 0.63 for noun-noun pairs and 0.52 for verb-verb pairs. When more pairs
were considered, the precision was still higher than that reported in previous studies.
Namely, for the top-500 pairs, the precision was 0.42 for adjective-adjective pairs, 0.33
for noun-noun pairs and 0.49 for verb-verb pairs.
This method needs less pre-processing steps than dependency patterns and can easily
be applied to vast data collections. The results can benefit many NLP applications
including augmentation of computational lexical resources, Contrast identification
[3,5], detection of paraphrases and contradictions [1] and others.
Corresponding author: a.lobanova@ai.rug.nl
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Syntactic Analysis of Dutch via Web Services
Tjong Kim Sang, Erik
University of Groningen
Alpino is a general-purpose syntactic parser for Dutch sentences. At this moment, using
the parser requires installation of the parser software at a local machine. In the CLARIN
project TTNWW, we develop a web service interface to the parser which will simplify
access for future users. The service provides access for client software via standard
protocols like SOAP and exchanges XML-encoded text data between the client machine
and the server where the parser is run. In this presentation, we present the current
status of this project.
Corresponding author: erikt@xs4all.nl

PRESENTATION ABSTRACTS
Abstract
Technology recycling between Dutch and Afrikaans
Augustinus, Liesbeth 1 and van Huyssteen, Gerhard 2 and Pilon, Suléne 3
1
Centre for Computational Linguistics (CCL), K.U. L
2
Centre for Text Technology (CTexT), North-West University,
Potchefstroom, South Africa
3
School for Languages, North-West University, Vanderbijlpark, South
Africa
Resource development for resource-scarce languages can be fast-tracked by recycling
existing technologies for closely-related languages. The main issue dealt with is the
recycling of Dutch technologies for Afrikaans. The possibilities of technology transfer
are investigated by focusing on the D2AC-A2DC project. After exploring the
architecture and functioning of D2AC, a Dutch-to-Afrikaans convertor, the attention
goes out to the development and performance of A2DC, an Afrikaans-to-Dutch
convertor. The latter tool is then used to improve the annotation of Afrikaans text with
Dutch technologies. In particular, the performance of part-of-speech tagging and
chunking has been considered. The accuracies of both tagger and chunker improve
significantly if the data are first converted with A2DC before they are sent through the
tools for Dutch analysis.
Corresponding author: liesbeth@ccl.kuleuven,be
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Technology recycling for closely related languages: Dutch
and Afrikaans
Abstract
Pilon, Suléne 1 and Van Huyssteen, Gerhard 2
1 North-West University (VTC)
2 North-West University (PC)
If two languages (L1 and L2) are similar enough, the development of technologies for
L2 can be expedited by recycling existing L1 resources. This process is called technology
recycling and the success thereof is greatly dependent on the degree of similarity
between the two languages in question. Other strategies can, however, be employed
to improve the efficiency of L1 technologies on L2 data and in this research we
experiment with one such strategy, viz. lexical conversion as pre-processing step. We
explore the possibility of using rule-based lexical conversion to improve the accuracy of
Dutch technologies when annotating Afrikaans data. The rationale here is that Dutch
technologies should perform better on Afrikaans data that appears more Dutch-like,
even if the conversion does not yield a good Dutch translation. To do the lexical
conversion, we developed an Afrikaans to Dutch convertor (A2DC) which obtains an
accuracy of more than 72% when converting Afrikaans words to Dutch. For our
experiment we use a state of the art Dutch POS tagger and parser to annotate raw
Afrikaans data. The same data is then converted with A2DC and once again annotated
with the Dutch technologies. In both experiments the conversion has a notably positive
effect on the performance of the Dutch technologies. The biggest difference is
observed in the POS tagging task with the overall accuracy increasing from 62.6% when
annotating raw Afrikaans data to 80.6% when annotating converted data, while the
parsing f-score improves from 0.44 (raw data) to more than 0.68 (converted data).
Corresponding author: sulene.pilon@nwu.ac.za

PRESENTATION ABSTRACTS
The more the merrier? How data set size and noisiness
affect the accuracy of predicting the dative alternation
Abstract
Theijssen, Daphne and van Halteren, Hans and Boves, Lou and
Oostdijk, Nelleke
Radboud University Nijmegen
In the dative alternation in English, speakers and writers choose between the
prepositional dative construction ('I gave the ball to him' and the double object
construction ('I gave him the ball'). Logistic regression models have already been shown
to be able to predict over 90% of the choices correctly (e.g. Bresnan et al. 2007).
Collecting dative instances from a corpus and encoding them with the required
information is a costly procedure. We therefore developed a semi-automatic approach
to do this, consisting of three steps: (1) automatically extracting dative candidates, (2)
manually approving or rejecting these candidates, and (3) automatically annotating the
approved candidates with the required information. The resulting data sets are noisier
than data sets that have been checked completely manually, but the approach can
yield much larger data sets.
We compare the effect of data set size and noisiness on the accuracy of predicting the
dative alternation. We employ a 'manual' set of 2,877 instances in spoken English,
taken from Switchboard (Godfrey et al. 1992) by Bresnan et al (2007) and from ICE-GB
(Greenbaum 1996) by Theijssen (2010). In addition, we use a 'semi-automatic' set with
7,755 instances from Switchboard, ICE-GB and BNC (BNC Consortium 2007). We
compare the learning curves of various machine learning algorithms by randomly
selecting subsets of the data and extending them with 500 instances each time. We do
this for different levels of noisiness, i.e. varying the proportion of 'semi-automatic'
instances (0%, 25%, 50%, 75%, 100%). The results are presented at the conference.
References
BNC Consortium (2007). The British National Corpus, version 3 (BNC XML Edition).
Oxford University Computing Services.
Bresnan Joan, Anna Cueni, Tatiana Nikitina and R. Harald Baayen (2007). Predicting the
Dative Alternation. In Bouma, Gerlof, Irene Kraemer and Joost Zwarts (eds.), Cognitive
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Foundations of Interpretation, Royal Netherlands Academy of Science, Amsterdam, pp
69-94.
Godfrey, John J., Edward C. Holliman and Jane McDaniel (1992). Switchboard:
Telephone speech corpus for research and development. Proceedings of the
International Conference on Acoustics, Speech, and Signal Processing (ICASSP-92), pp.
517–20.
Greenbaum, Sidney (ed.) (1996). Comparing English Worldwide: The International
Corpus of English. Oxford, U.K.
Theijssen, Daphne (2010). Variable selection in Logistic Regression: The British English
dative alternation. In Icard, Thomas and Reinhard Muskens (eds.), Interfaces:
Explorations in Logic, Language and Computation. Series: Lecture Notes in Computer
Science (subseries: Lecture Notes in Artificial Intelligence), volume 6211, Springer.
Corresponding author: d.theijssen@let.ru.nl

PRESENTATION ABSTRACTS
The use of structure discovery methods to detect syntactic
change
Abstract
ten Bosch, Louis and Versteegh, Maarten
Radboud University Nijmegen
A well-known problem in linguistics deals with the description and analysis of
diachronic changes in syntactic constructions. In Western European languages, such
changes have occurred a number of times over the last few centuries. In this
presentation we present an overview of quantitative methods for analyzing historical
text corpora. Our overview will include parsing-related methods, Bayesian methods
and Latent Semantic Analysis, with special focus on methods that do not take syntactic
trees as a starting point. In addition, we will pay attention to two methodological
approaches, viz. the contrastive approach, according to which two different
independent analyses of two corpora are compared, and the single-model approach,
according to which changes in syntactic structure are interpreted as the result of a
(possibly biased) competition within a single model.
We will compare the various methods by presenting different analyses of the same text
material.
Corresponding author: l.tenbosch@let.ru.nl
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Treatments of the Dutch verb cluster in formal and
computational linguistics
Van Eynde, Frank
K.U.Leuven
The Dutch verb cluster has always been a challenge for formal and computational
linguistics, since the sentences which contain one display a rather dramatic discrepancy
between surface structure, on the one hand, and semantic structure, on the other
hand, as illustrated amongst others by the cross-serial dependencies in sentences with
an AcI verb, such as 'zien' in '...dat ik haar de honden heb zien voederen' (... that I saw
her feed the dogs).
In multistratal frameworks, such as transformational grammar, the discrepancy is
accounted for in terms of movement. More specifically, there is a level of syntactic
structure which straightforwardly reflects the semantic relations, called deep structure
or D-structure, and there is a series of transformations which map D-structures onto
surface structures. The transformations either move the verbs, as in Arnold Evers'
analysis, or their arguments, as in Jan-Wouter Zwart's analysis.
In monostratal frameworks, such as GPSG and HPSG, the discrepancy between surface
stucture and semantic structure is handled in terms of the inheritance of valence
requirements, allowing the verbs in the cluster to take over the unfulfilled valence
requirements of their verbal complement. This approach was pioneered by Mark
Johnson in GPSG and by Erhard Hinrichs and Tsuneko Nakazawa in HPSG. Applications
to Dutch are spelled out in work by Gerrit Rentier and by Gosse Bouma and Gertjan van
Noord.
In the Dutch treebanks, such as those of CGN and Lassy, the treatment of the verb
cluster is monostratal, but the device to bridge the discrepancy between surface
structure and semantic structure is more reminiscent of multistratal analyses, allowing
the existence of crossing dependencies and hence the postulation of discontinuous
constituents. The talk will give a survey of the existing treatments and provide a
comparative evaluation.
Corresponding author: frank.vaneynde@ccl.kuleuven.be

PRESENTATION ABSTRACTS
TTNWW: de facto standards for Dutch in the context of
CLARIN
Abstract
Schuurman, Ineke 1 and Marc Kemps-Snijders 2
1 Centrum voor Computerlinguïstiek, K.U.Leuven
2 Meertens Instituut, Amsterdam
The Flemish and Dutch CLARIN groups have started TTNWW, a larger pilot project
(2010-2012), in which several existing resources (both text and speech) and the facto
standards for Dutch are a) used to help HSS researchers to address new research
needs, while b) these resources and standards are adapted to/mapped onto the
standards adopted by CLARIN, embedded in a work flow and presented as a web
service designed for these HSS researchers.
In TTNWW technology partners (5 speech, 10 text) and user groups (4 speech, 2 text)
are involved, spread over Flanders and the Netherlands
The text part of TTNWW focusses on recognition of all kinds of names in various types
of texts, such as Dutch novels and archaeological documents, the latter case in
combination with temporal analysis. All 'lower' levels are also taken care of.
The project will provide the CLARIN community with ample feedback with respect to
the standards and technologies proposed in the European context and promote the de
facto standards for Dutch NLP as used in CGN and several STEVIN-projects.
In this presentation we will concentrate on various standards for written language and
mapping between these, especially PoS (CGN/D-Coi), MAF and ISOcat.
Corresponding author: ineke.schuurman@ccl.kuleuven.be
71

Abstract
72
CLIN 21 – CONFERENCE PROGRAMME
TTNWW: NLP Tools for Dutch as Webservices in a
Workflow
Kemps-Snijders, Marc 1
Ineke Schuurman 2
1 Meertens Instituut, Amsterdam
2 CCL, K.U.Leuven
The Flemish and Dutch CLARIN groups have started TTNWW, a larger pilot project
(2010-2012), in which several existing resources (both text and speech) and the facto
standards for Dutch are a) used to help HSS researchers to address new research
needs, while b) these resources and standards are adapted to/mapped onto the
standards adopted by CLARIN, embedded in a work flow and presented as a web
service designed for these HSS researchers.
In TTNWW technology partners (5 speech, 10 text) and user groups (4 speech, 2 text)
are involved, spread over Flanders and the Netherlands.
To develop the functionalities for both the speech and text parts of the project services
that are delivered by each of the partners will be combined in a workflow approach
allowing for flexible combinations of processes. Efforts in this area are embedded in
the CLARIN effort to describe web services for easy discovery and profile matching, i.e.
offering possible combinations of available resources and web services for specific
tasks.
In this presentation we will focus on methods for workflow construction, description of
web services and place them in the international perspective of CLARIN.
Corresponding author: marc.kemps.snijders@meertens.knaw.nl

PRESENTATION ABSTRACTS
Using corpora tools to analyze gradable nouns in Dutch.
Abstract
Ruiz, Nicholas and Weiffenbach, Edgar
University of Groningen
Morzycki (2009) claims that degree readings of size adjectives, such as "a big idiot" are
not merely the "consequence of some extragrammatical phenomenon," but rather can
be attributed to syntax, which gives positional restrictions on the availability of degree
readings. We expand on Morzycki (2009) by introducing a corpus-based analysis in
Dutch to verify Morzycki's claims and to extend his claim to the semantic domain.
Using LASSY, a syntactically annotated Dutch corpus developed inter alia under the
STEVIN programme, we extract syntactic and semantic properties of noun phrases
consisting of adjectives "gigantisch", "kolossaal", and "reusachtig" and manually
annotate each adjective-noun pair with a gradable or non-gradable label.
Using these features, we construct a statistical model based on logistic regression and
find that the semantic role, definiteness, and particular semantic noun groups derived
from Cornetto (a Dutch WordNet with referential relations) have a significant effect on
the likelihood that an adjective-noun pair is interpreted by the reader to have a degree
reading.
Corresponding author: nicholas.ruiz@gmail.com
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Using easy distributed computing for data-intensive
processing
Abstract
Van den Bogaert, Joachim
Centre for Computational Linguistics, K.U. Leuven
Given the large amounts of data we are coping with when computing useful data from
large corpora, and the difficulties and costs it takes to run parallel code with traditional
parallel computing, we will present different frameworks that may be used to facilitate
easy distributed computing. Using string-to-tree alignment (GHKM), frequent subtree
mining, and distributed Moses decoding as example cases, we will demonstrate how
applications and algorithms may be upscaled and out-scaled with these frameworks.
We will consider both the creation of an embarrassingly parallel solution and the redesign
of an existing algorithm to fit the mapreduce paradigm.
Corresponding author: joachim@ccl.kuleuven.be

PRESENTATION ABSTRACTS
Abstract
What is the use of multidocument spatiotemporal
analysis?
Schuurman, Ineke and Vandeghinste, Vincent
Centrum voor Computerlinguïstiek, K.U.Leuven
In the project AMASS++ (IWT) the central research topic concerns multilingual
multimedia multidocument summarization: how, in a huge digitized newsarchive, can a
journalist find documents dealing with the same events and have a summary made of
them in order to assess their usefulness for a specific purpose? Or: How can a news
paper deliver personalized (inter)national and local news to a specific subscriber?
In this presentation we will show how spatiotemporal analysis can be of assistance in
such tasks, even when the input just consists of raw PoS-tagged texts (i.e. contrary to
the SoNaR-project where many levels of annotation are available, all of them
corrected).
Corresponding author: ineke.schuurman@ccl.kuleuven.be
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Without a doubt no uncomplicated task: Negation cues
and their scope
Abstract
Morante, Roser and Schrauwen, Sarah and Daelemans, Walter
CLiPS - University of Antwerp
Although negation has been extensively treated from a theoretical perspective (Klima
1964, Horn 1989, Tottie 1991, van der Wouden 1997) and it's processing is thought to
be relevant for natural language processing systems (Morante and Sporleder 2010),
there is a lack of annotated resources and no publicly available annotation guidelines
can be found that describe in detail how to annotate negation related aspects. In this
talk we present a corpus annotated with negation cues and their scope, we describe
the guidelines that we have defined and we comment on the linguistic aspects of the
annotation process. The annotated corpus contains the detective stories The Hound of
the Baskervilles and The Adventure of Wisteria Lodge by Conan Doyle. Part of the
corpus has already been annotated with other layers of semantic information
(semantic roles, coreference) for the SemEval Task Linking Events and Their
Participants in Discourse (Ruppenhofer et al., 2010). We first describe the expression
of negation in this corpus and compare it with the expression of negation in biomedical
documents. Then we comment in detail several aspects related to the negation
phenomenon: how to determine what negation cues are, how to mark the scope, and
how to determine whether an event is negated. We will show that marking the cues is
not a matter of lexical look-up because some cues are ambiguous, and that contextual
and discourse level features play a role in finding the scope. Additionally, we show
that finding negated events depends on the semantic class of the predicates being
involved, their mood and tense: on the modality of the event clause and on the
syntactic constructions. Finally, we comment on the most difficult aspects of the
annotation process, like determining when prepositions like "save" or "except" act as
negation cues.
Corresponding author: roser.morante@ua.ac.be

Poster Abstracts
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A database for lexical orthographic errors in French
Abstract
Manguin, Jean-Luc
GREYC - Univ.de Caen - France
This work describes the construction of a database for lexical orthographic errors in
French. This construction uses different techniques form the field of NLP for a goal in
the field of psycholinguisitics. In psycholinguisitics, it is often difficult and long to
collect enough data from experiments with real people. Here the data are collected online
and come from the requests made to an on-line dictionary. In this huge amount of
data (about 160 millions words, 4 millions distinctive forms), we can find enough errors
to have good statistics for a deep study of errors. The questions developped here are
the link between a "bad" form and its correction, and the classification of errors in a
small number of types. Several programs and techniques are involved to achieve these
tasks : detection of graphic neighbours, phonetization, pattern matching : the
combination of these techniques leads us to 70% of correction with no ambiguity, and
80% if we accept the system give several possible corrections. The classification of
errors is also useful for predicting where errors may appear in the words, and thus for
the knowledge of children's learning of orthography.
Corresponding author: jean-luc.manguin@unicaen.fr

POSTER ABSTRACTS
Abstract
A Posteriori Agreement as a Quality Measure for
Readability Prediction Systems
van Oosten, Philip and Hoste, Véronique and Tanghe, Dries
LT3, University College Ghent
All readability research is ultimately concerned with the research question whether it is
possible for a prediction system to automatically determine the level of readability of
an unseen text.
A significant problem for such a system is that readability might depend in part on the
reader.If different readers assess the readability of texts in fundamentally different
ways, there is insufficient a priori agreement to justify the correctness of a readability
prediction system based on the texts assessed by those readers.We built a data set of
readability assessments by expert readers.We clustered the experts into groups with
greater a priori agreement and then measured for each group whether classifiers
trained only on data from this group exhibited a classification bias.
As this was found to be the case, the classification mechanism cannot be
unproblematically generalized to a different user group.
Corresponding author: philip.vanoosten@hogent.be
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A TN/ITN Framework for Western European languages
Abstract
Chesi, Cristiano 1 and Cho, Hyongsil 2 andBaldewijns, Daan 2 and Braga,
Daniela 1
1 Microsoft Language Development Center
2 Microsoft Language Development Center, ISCTE-Lisbon University
Institute, Portugal
(Inverse) Text Normalization, (I)TN, is an essential module in Text-to-Speech (TTS) and
Speech Recognition (SR) systems and it requires both a significant development
timeline and a deep linguistic expertise (Mikheev 2000, Palmer 2010).
In this work, we describe an efficient multilingual (I)TN framework that is rule-based,
hierarchical and modular: the core system is composed by a large set of optimized
Finite-State Transducers (FSTs) that are compiled following the Normalization Maps
developed by Language Experts (LEs) for each language: such maps are built using a
proprietary tool (TNAuthoringTool, Patent Serial No. 12/361,114) that allows the LEs to
express terminals normalization at high level (e.g. Term_1: “1” > “one”) and easily
combine such terminals by means of hierarchical, weighted rules: these rules can be
ordered sets of terminals or other rules, each one ranked according to their relevance
so as to prevent interference in specific contexts (e.g. Rule_1: “21-12-2010” > “twentyfirst
of december two thousand ten“ vs. Rule_2: “23-29” > “from twenty three to
twenty nine”). Such rules are clustered under a small number of Top-Level rules that
will be the entry states of the compiled FSTs.
The core set of Top-Level rules developed covers Numerals, Ordinals, Dates and Time,
Telephone numbers, Measurements and Web-related terms (e.g. URLs, email,
acronyms). Here, we focus on the ambiguity resolution implemented in three
languages (English, French, Italian) in the normalization of web-search specific terms
and mobile text messages. Accuracy and coverage of the FSTs are evaluated against
very large BING queries collections and SMS corpora.
Corresponding author: v-crches@microsoft.com

POSTER ABSTRACTS
Abstract
An Examination of Cross-Cultural Similarities and
Differences from Social Media Data with respect to
Language Use
Elahi, Mohammad Fazleh and Monachesi, Paola
We present a methodology for analyzing cross-cultural similarities and differences
using language as a medium, love as domain, social media as a data source and 'Terms'
(emotions and sentiments) and 'Topics' as cultural features. We discuss the techniques
necessary for the creation of the social data corpus from which emotion terms have
been extracted using NLP techniques. Topics of love discussion were then extracted
from the corpus by means of Latent Dirichlet Allocation (LDA). Finally, on the basis of
these features, a cross-cultural comparison was carried out. For the purpose of crosscultural
analysis, the experimental focus was on comparing data from a culture from
the East (India) with a culture from the West (United States of America). Similarities
and differences between these cultures have been analyzed with respect to the usage
of emotions, their intensities and the topics used during love discussion in social media.
Findings include (i) Indians are more emotional than Americans but Americans express
themselves with stronger emotion terms than Indians, (ii) In discussions on common
topics related to love (Wedding, Same Sex etc), the conversations of Indians and
Americans are related to the particular traditions and recent issues of their culture, and
(iii) Indians and Americans also use some terms and topics,which are only related to
their culture.
Corresponding author: rmf_ku@yahoo.com
81

Abstract
82
Authorship Verification of Quran
Shokrollahi-Far, Mahmoud
Tilburg University
CLIN 21 – CONFERENCE PROGRAMME
Holy Quran, as the cultural heritage of the Islamic world, has long been the focus of
scholarly disputes some not solved so far, such as whether Prophet Mohammad
himself has authored the book. This paper reports a research trend approaching such
disputes as a text classification task, such as authorship verification. To induce some
classifiers for this verification task, SVM and Naive Bays machines have been trained on
the tagged corpora of Quranic texts bootstrapped by Mobin, a morpho-syntactic tagger
developed for Arabic. The algorithm applied for the task is an efficient enhancement of
the algorithms applied so far for authorship verification. This algorithm seems
applicable for authorship problems of the other texts in Arabic. The results have not
verified the authorship of Quran by Prophet Mohammad.
Corresponding author: m.shokrollahifar@uvt.nl

POSTER ABSTRACTS
CLAM: Computational Linguistics Application Mediator
Abstract
van Gompel, Maarten and Reynaert, Martin and van den Bosch, Antal
TiCC, Tilburg University
The Computational Linguistics Application Mediator (CLAM) allows you to quickly and
transparently transform your Natural Language Processing application into a RESTful
webservice, with which automated clients can communicate, but which at the same
time also acts as a modern webapplication with which human end-users can interact
directly. CLAM takes a description of your system and wraps itself around the system. It
allows both automated clients and human end-users to upload input files to your
application, start your application with specific parameters, and download or directly
view the output files produced by your application after it has completed execution.
Rich support for metadata and provenance data is also provided.
CLAM is set up in a universal fashion, making it flexible enough to be wrapped around a
wide range of computational linguistic applications. These applications are treated as a
black box, of which only the parameters, input formats, and output formats need to be
described. The applications themselves need not be network-aware in any way, nor
aware of CLAM. The handling and validation of input is taken care of by CLAM.
Corresponding author: proycon@anaproy.nl
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Discriminative features in reversible stochastic attributevalue
grammars
Abstract
de Kok, Daniël
University of Groningen
Reversible stochastic attribute-value grammars use one model for parse
disambiguation and fluency ranking. Such a model encodes preferences with respect to
syntax, fluency, and appropriateness of logical forms, as weighted features. This
framework is appropriate if similar preferences are used in parsing and generation.
Reversible models incorporate features that are specific to parse disambiguation and
fluency ranking, as well as features that are used for both tasks. One particular concern
with respect to such models is that much of their discriminatory power is provided by
task-specific features. If this is true, the premise that similar preferences are used in
parsing and generation does not hold.
A detailed analysis of features could give us more insight into the true reversibility of
stochastic attribute-value grammars. However, as De Kok (2010) argued, such featurebased
models are very opaque due to their enormous size and the tendency to spread
weight mass among overlapping features. Feature selection methods can be used to
extract a subset of features that do not overlap.
In this work, we compare gain-informed feature selection (Berger et al., 1996: Zhou et
al., 2003: De Kok, 2010), grafting (Perkins et al, 2003), and grafting-light (Zhu et al.,
2010) in performing selection on reversible models. We then use the most effective
method to extract a list of features ranked by their discriminatory power. We show
that only a very small number of features is required to produce an effective model for
parsing and generation. We also provide a qualitative and quantitative analysis of these
features.
Corresponding author: d.j.a.de.kok@rug.nl

POSTER ABSTRACTS
Abstract
Fietstas: a web service for text analysis
Jijkoun, Valentin and de Rijke, Maarten and Vishneuski, Andrei
University of Amsterdam
We present Fietstas: a open-access web service for text analysis, created with the idea
of simplifying building text-intensive applications. As a web service, Fietstas consists of
(1) a simple content management component, where users can upload their content
with metadata, (2) a collection of text processing components, from tokenization to
named entity extraction and normalization, (3) a component for accessing/visualizing
document processing results (e.g., as XML or HTML), and (4) data analysis component
for generating term-cloud-based summaries and timelines. The functionality is
available through easy-to-use REST interface (i.e., through standard HTTP requests),
and moreover, a number of APIs are available (e.g., for Python and Perl). In this
presentation, we briefly describe Fietstas and demonstrate how its functionality can be
used in a simple web application (news search and analysis).
Corresponding author: jijkoun@uva.nl
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Abstract
86
CLIN 21 – CONFERENCE PROGRAMME
FoLiA: Format for Linguistic Annotation
van Gompel, Maarten and Reynaert, Martin and van den Bosch, Antal
TiCC, Tilburg University
We present FoLiA, an XML-based annotation format suitable for the representation of
written language resources. The format builds upon the work put in the D-Coi/SoNaR
format, but greatly extends this to accommodate a wide variety of linguistic
annotations. The objective is to present a rich annotation format based on a single
unifying notation paradigm that does not commit to any particular tagset, but instead
offers maximum flexibility and extensibility. In doing so, we replace the many ad-hoc
formats present in the field with a single well-structured format.
FoLiA will be proposed as a candidate CLARIN-standard.
Corresponding author: proycon@anaproy.nl

POSTER ABSTRACTS
How can computational linguistics help determine the
core meaning of then in oral speech?
Abstract
Vallee, Michael
EDC
The research on the connective then mainly focuses on the temporal interpretation of
it. However, little has been done on then in oral speech and more precisely in
questions, orders or inferential sentences. It seems important to show that
computational linguistics can really help determine if there is one way to describe the
connective in these contexts or if there are as many different connectives then as there
are linguistic structures.
To do so, I will use the prosody of questions and sentences in oral speech to
demonstrate that the speaker expresses a surprise or a contradiction with what was
uttered prior the connective in these contexts. To illustrate this perspective, let’s
consider the following utterance “Now then you listen to me”. I will show that the
phonological structure shown by a software helps to describe how then works. For
instance, in the example above, the underlying structure was “you-not-listen to me”
before the utterance which was not expected by the speaker. In that case, the
connective shows the different viewpoints between the speaker and the hearer.
Corresponding author: m.vallee@yahoo.fr
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88
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Of mathematicians and physicists: the history of language
and speech technology in the Netherlands and Flanders
Abstract
van der Beek, Leonoor
Q-go
When did language technology came into being in the Low Countries? Why are
language and speech technology (LST) located in different Faculties in Dutch and
Flemish universities? What was the impact of Lernout and Hauspie on the LST industry
in the Netherlands? From September 2009 until September 2010, I investigated these
and many other questions related to the history of LST in the Netherlands and
Flanders. I interviewed the pioneers in our field and compiled from their stories a
diverse picture of struggle against the limitations of immature computer technology, of
boundless optimism and deep disappointment, and of academic friendships and fights.
From Adriaan van Wijngaarden to Jo Lernout and from PHLIQA via Eurotra to CGN. I'll
sketch the project and the approach I've taken, and give away some of the highlights of
the book `Van Rekenmachine tot Taalautomaat' (in Dutch), which will tell the full story.
Corresponding author: vdbeek@gmail.com

POSTER ABSTRACTS
Abstract
On the difficulty of making concreteness concrete
van Halteren, Hans and Theijssen, Daphne and Oostdijk, Nelleke and
Boves, Lou
Radboud University Nijmegen
As analysis and annotation progresses to deeper linguistic levels, matters prove ever
more difficult. It not only becomes harder to get machines to provide proper analyses,
but also to define exactly what we want. Whereas there appears to be consensus on
what plural nouns are (morpho-syntax) or what relative clauses are (syntax), this is
certainly not the case for semantic properties like concreteness. When reading papers
referring to such concepts, one is unlikely to notice any problems. Bresnan et al.
(2007), e.g., just use concreteness of a noun as a given and draw conclusions about the
significance of its influence on choices in the dative alternation.
However, once we ourselves attempt to annotate for concreteness, we run headlong
into the absence of any clear definition of concreteness. Bresnan refers to Garretson
(2003), where all we get is a vague (and somewhat circular) description and some
examples. Looking further, we find lists, such as in the MRC Psycholinguistic Database
(Coltheart, 1981), as well as procedures, such as Xing et al.’s (2010) procedure based
on WordNet, all apparently leading to values for the property concreteness. But we can
only wonder to which degree these various definitions/procedures lead to the same
results. In this paper, therefore, we take a number of concreteness value yielding
procedures and examine a) to which degree they overlap in their annotation of corpus
data (here: Semcor) and b) to which degree they lead to the same conclusions about
the influence of concreteness on syntactic processes (here: dative alternation).
Corresponding author: hvh@let.ru.nl
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Abstract
90
CLIN 21 – CONFERENCE PROGRAMME
ParaSense or how to use parallel corpora for Cross-
Lingual Word Sense Disambiguation
Lefever, Els and Hoste, Véronique
LT3, University College Ghent
Cross-Lingual Word sense disambiguation (WSD) consists in selecting the correct
translation of an ambiguous word in a given context. In this talk we present a set of
experiments for a classification-based WSD system that uses evidence from multiple
languages to define a translation label for an ambiguous target word in one of the five
supported languages (viz. Italian, Spanish, French, Dutch and German). Instead of using
a predefined monolingual sense-inventory such as WordNet, we use a languageindependent
framework and build up our sense inventory by means of the aligned
translations from the parallel corpus Europarl.
The information that is used to train and test our classifier contains the well known
WSD local context features of the English input sentences, as well as translation
features from the other languages.
Our results show that the multilingual approach outperforms the classification
experiments that merely take into account the more traditional monolingual WSD
features. In additon, our results are competitive with those of the best systems that
participated in the SemEval-2 "Cross-Lingual Word Sense Disambiguation" task.
Corresponding author: els.lefever@hogent.be

POSTER ABSTRACTS
Abstract
"Pattern", a web mining module for Python
De Smedt, Tom and Daelemans, Walter
CLiPS, University of Antwerp
"Pattern" is a mash-up package for the Python programming language that bundles
fast, regular expressions-based functionality for NLP and data-mining tasks. It consists
of the following modules:
1) pattern.web: provides easy access to Google, Yahoo, Bing, Twitter, Wikipedia,
Flickr, RSS + a robust HTML DOM parser.
2) pattern.en: tools for verb inflection, noun pluralization/singularization, a WordNet
interface, a fast tagger/chunker based on regular expressions.
3) pattern.table: for working with datasheets (e.g. MS Excel) and CSV-files.
4) pattern.search: regular expressions for syntax and semantics. For example:
"BRAND|NP VP JJ+" matches any sentence in which a noun phrase containing a
brand name is followed by a verb phrase followed by one or more adjectives, e.g.
"the new iPhone will be amazing", "Doritos taste cheesy", ...
5) pattern.vector: corpus tools for tf-idf, cosine similarity, vector space search and
LSA.
6) pattern.graph: for exploring graphs and semantic networks.
The package can be used and extended for harvesting online data, opinion mining,
building semantic networks using a machine learning approach, and so on.
Corresponding author: tom.desmedt@ua.ac.be
91

Abstract
92
CLIN 21 – CONFERENCE PROGRAMME
Semantic role labeling of gene regulation events
Morante, Roser
CLiPS,- University of Antwerp
This poster describes work in progress on semantic role labeling of gene regulation
events. Semantic role labeling (SRL) is a natural language processing task that consists
of identifying the arguments of predicates within a sentence and assigning a semantic
role to them (Màrquez et al., 2008). This task can support the extraction of relations
from biomedical texts. Recent research has produced a rich variety of SRL systems to
process general domain corpora. However, less systems have been developed to
process biomedical corpora (Tzong-Han Tsai et al, 2007: Bethard et al., 2008). In this
abstract, we present preliminary results of a new system that is trained on the GREC
corpus (Thompson et al., 2009). The system performs argument identi?cation and
semantic role assignment in a single step, assuming gold standard event identi?cation.
We provide cross-validation and cross-domain results.
Corresponding author: roser.morante@ua.ac.be

POSTER ABSTRACTS
Abstract
Source Verification in Quran
Shokrollahi-Far, Mahmoud
Tilburg University
The revelation of Holy Quran has been accomplished either in Mecca or in Medina, for
which some chapters or even verses of the book have been classified as either Meccan
or Medinan. This crucial classification helps the scholars of Quran in so many topics
including the exegesis of the book. Among the one hundred and fourteen chapters of
Quran there are still thirty-two disagreed on to be whether Meccan or Medinan. More
deeply, the scholars have long disputed on the features that would discriminate
between these two classes. This paper reports a research trend on applying text
classification tasks, say source verification, to help the resolution of such Quranic
disputes. For this binary TC task, some classifiers have been induced by training SVM
and Naive Bays machines on the tagged corpora of Quranic texts bootstrapped by
Mobin, a morpho-syntactic tagger developed for Arabic. This research has not only
explored the required distinctive grammatical features, but also led to a successful
classification of the disputed chapters and verses as Meccan or Medinan.
Corresponding author: m.shokrollahifar@uvt.nl
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94
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Towards a language-independent data-driven compound
decomposition tool
Abstract
Réveil, Bert 1 and Macken, Lieve 2
1 ELIS, Ghent University
2 LT3, Language and Translation Technology Team
Compounding is a highly productive process in Dutch that poses a challenge for various
NLP applications such as terminology extraction, continuous speech recognition, and
automated word alignment. The present work therefore proposes a languageindependent,
data-driven decomposition tool that tries to segment compounds into
their meaningful parts.
The basic version of this tool initially determines a list of eligible compound
constituents (so-called heads and tails), relying solely on word frequency information
that is extracted from a large text corpus. The decomposition algorithm then
recursively attempts to decompose the compounds, allowing only two-part head-tail
divisions in each iteration. E.g. the noun 'postzegelverzamelaar' is first split into
'postzegel' + 'verzamelaar', followed by an additional decomposition of 'postzegel' into
'post' + 'zegel'.
Apart from the basic version, an extended version of the tool is assessed that uses PoS
information as a means to restrict the list of possible heads and tails. The preformance
of both versions is evaluated in two large-scale decomposition experiments, one on the
E-lex compound list and one on a word list that contains specific vocabulary from the
automotive domain. As the presented decomposition tool only relates on word
frequency and PoS information, it is expected that the tool can be easily adapted to
new domains and languages.
Corresponding author: breveil@elis.ugent.be

POSTER ABSTRACTS
Towards improving the precision of a relation extraction
system by processing negation and speculation
Abstract
Van Asch, Vincent and Morante, Roser and Daelemans, Walter
CLiPS - University of Antwerp
In this poster we present BiographTA, a system that extracts biological relations from
PubMed abstracts. The relation extraction system has been designed to process
abstracts in which biological relations from multiple databases have been annotated
automatically based on an in-sentence co-occurrence criterium. It performs a relation
identification task learning from noisy data, since a proportion of the automatically
annotated relations in the training corpus is incorrect. The system cannot be evaluated
on noisy data. For this reason, in order to develop and evaluate the system, we gather
a corpus of PubMed abstracts annotated with the gold biological relations of the
Bioinfer corpus.
Additionally, one of the text mining goals in the Biograph project is to develop
techniques that allow to perform large scale relation extraction starting from the
smallest possible amount of manually annotated data and obtaining the highest
precision possible. This is why we add to the relation extraction system a module that
processes negation and speculation cues. We present experiments aimed at testing
whether processing the scope of negation and speculation cues results in a higher
precision of the relations extracted. Results show that the negation and speculation
detection module increases the precision in 2.93 at the cost of decreasing recall in 0.68.
Corresponding author: Vincent.VanAsch@ua.ac.be
95

List of Participants
97

LIST OF PARTICIPANTS
Liesbeth Augustinus liesbeth@ccl.kuleuven.be CCL - K.U. Leuven
Daan Baldewijns v-daanb@microsoft.com Microsoft Language
Development Center
Kim Bauters kim.bauters@ugent.be Ghent University
Richard Beaufort richard.beaufort@uclouvai
n.be
UCL CENTAL
Peter Berck P.J.Berck@UvT.nl TiCC, Tilburg University
Tamas Biro t.s.biro@uva.nl ACLC, University of
Amsterdam
Jelke Bloem j.bloem.3@student.rug.nl University of Groningen
Cristiano Chesi v-crches@microsoft.com Microsoft Language
Development Center,
Porto Salvo - ISCTE-Lisbon
University Institute,
Portugal
Kostadin Cholakov k.cholakov@rug.nl University of Groningen
Louise-Amélie Cougnon louiseamelie.cougnon@uclouvai
n.be
CENTAL - IL&C, UCLouvain
Crit Cremers c.l.j.m.cremers@hum.leide
nuniv.nl
Leiden University
Walter Daelemans walter.daelemans@ua.ac. CLiPS, University of
be
Antwerp
Orphée De Clercq orphee.declercq@hogent. LT3, University College
be
Ghent
Martine De Cock Martine.DeCock@UGent.b
e
Ghent University
Daniël de Kok d.j.a.de.kok@rug.nl University of Groningen
Tom De Smedt tomdesmedt@gmail.com CLiPS Universiteit
Antwerpen
Herwig De Smet herwig.desmet@kdg.be OptiFox 7th Framework
Europe
Dennis de Vries dennis@gridline.nl GridLine
Saskia Debergh saskia.debergh@intersyste
ms.com
i.Know nv
Johannes Deleu johannes.deleu@intec.uge IBCN, IBBT & Ghent
nt.be
University
Thomas Demeester thomas.demeester@ugent
.be
Ghent University
Bart Desmet bart.desmet@hogent.be LT3, University College
Ghent
99

100
CLIN 21 – CONFERENCE PROGRAMME
Brecht Desplanques brecht.desplanques@elis.u
gent.be
ELIS, Ghent University
Peter Dirix peter.dirix@nuance.com Nuance
Stephen Doherty stephen.doherty2@mail.d
cu.ie
Marius Doornenbal m.doornenbal@elsevier.co
m
Frederik Durant frederik.durant@tomtom.
com
Mohammad
Fazleh
Elahi rmf_ku@yahoo.com
Dublin City University
Reed Elsevier
TomTom
Thomas François thomas.francois@uclouvai
n.be
CENTAL, UCLouvain
Tanja Gaustad T.Gaustad@uvt.nl TiCC, Tilburg University
Olga Gordeeva ogordeeva@gmail.com Acapela Group
Kris Heylen kris.heylen@arts.kuleuven
.be
QLVL, K.U.Leuven
Maarten Hijzelendoorn p.m.hijzelendoorn@hum.l
eidenuniv.nl
Leiden University
Veronique Hoste veronique.hoste@hogent. LT3, University College
be
Ghent
Steve Hunt s.j.hunt@tilburguniversity.
nl
TiCC, Tilburg University
Marc Kemps-Snijders marc.kemps.snijders@me
ertens.knaw.nl
Meertens Instituut
Mike Kestemont mike.kestemont@ua.ac.be CLiPS, University of
Antwerp
Maxim Khalilov maxkhalilov@gmail.com ILLC, University of
Amsterdam
Henny Klein E.H.Klein@rug.nl University of Groningen
Cornelis H.A. Koster kees@cs.ru.nl Radboud Universiteit
Nijmegen
Gideon Kotzé g.j.kotze@rug.nl University of Groningen
Mark Kroon mark.kroon@actonomy.co
m
Actonomy
Reinier Lamers lamers@textkernel.nl Textkernel
Els Lefever els.lefever@hogent.be LT3, University College
Ghent
Anna Lobanova a.lobanova@ai.rug.nl AI, University of Groningen
Alessandro Lopopolo A.Lopopolo@student.uva. ACLC, University of
nl
Amsterdam
Kim Luyckx kim.luyckx@ua.ac.be CLiPS, University of
Antwerp
Lieve Macken lieve.macken@hogent.be LT3, University College

LIST OF PARTICIPANTS
Gideon Maillette de
Buy Wenniger
Ghent
gemdbw@gmail.com ILLC, University of
Amsterdam
Véronique Malaisé vmalaise@vu.nl VU University Amsterdam
Jean-Luc Manguin jeanluc.manguin@unicaen.fr
CNRS - Université de Caen
Eliza Margaretha e.margaretha@student.ru
g.nl
University of Groningen
Thomas Markus Thomas.Markus@phil.uu.n
l
Utrecht University
Scott Martens scott@ccl.kuleuven.be CCL, K.U.Leuven
Dieneke Meijer dieneke.meijer@agentsch
apnl.nl
Agentschap NL / STEVIN
Sien Moens sien.moens@cs.kuleuven.
be
K.U.Leuven CW
Paola Monachesi P.Monachesi@uu.nl Utrecht University
Roser Morante roser.morante@ua.ac.be CLiPS, University of
Antwerp
Peter Nabende p.nabende@rug.nl University of Groningen
Fabrice Nauze fabrice.nauze@rightnow.c
om
Q-go / Rightnow
John Nerbonne j.nerbonne@rug.nl University of Groningen
Jan Odijk j.odijk@uu.nl UiL-OTS Universiteit
Utrecht
Leequisach Panjaitan leequisach.panjaitan@yah
oo.com
Claudia Peersman claudia.peersman@ua.ac.b
e
CLiPS, University of
Antwerp & Artesis
Suléne Pilon sulene.pilon@nwu.ac.za North-West University
(VTC)
Barbara Plank b.plank@rug.nl University of Groningen
Massimo Poesio poesio@essex.ac.uk University of Essex
Bert Réveil breveil@elis.ugent.be DSSP, ELIS, Ghent
University
Mihai Rotaru rotaru@textkernel.nl Textkernel
Nicholas Ruiz nicholas.ruiz@gmail.com University of Groningen
Marijn Schraagen schraage@liacs.nl Leiden University
louise schubotz louise_schubotz@gmx.de Radboud University
Nijmegen
Ineke Schuurman ineke.schuurman@ccl.kule
uven.be
CCL, K.U.Leuven
101

102
CLIN 21 – CONFERENCE PROGRAMME
Roxane Segers r.h.segers@vu.nl VU University Amsterdam
Binyam Seyoum binephrem@gmail.com Addis Ababa University
Margaux Smets margauxsmets@gmail.com QLVL, K.U.Leuven
Martijn Spitters spitters@textkernel.nl Textkernel
Peter Spyns pspyns@taalunie.org Nederlandse Taalunie
Tim Stokman timstokman@gmail.com Textkernel
Dries Tanghe dwiesje@hotmail.com LT3, University College
Ghent
Tristan
Thomas
Teunissen tristan@w3lab.nl BA
Daphne Theijssen d.theijssen@let.ru.nl Radboud University
Nijmegen
Erik Tjong Kim Sang erikt@xs4all.nl University of Groningen
Fabian Triefenbach fabian.triefenbach@elis.ug
ent.be
ELIS, Ghent University
Frederik Vaassen frederik.vaassen@ua.ac.be CLiPS, University of
Antwerp
Michaël Vallée m.vallee@yahoo.fr EDC Paris
Vincent Van Asch Vincent.VanAsch@ua.ac.b CLiPS, University of
e
Antwerp
Matje van de Camp M.M.v.d.Camp@uvt.nl TiCC, Tilburg University
Tim Van de Cruys tv234@cam.ac.uk University of Cambridge
Marjan Van de Kauter marjan.vandekauter@hog LT3, University College
ent.be
Ghent
Anne van de
amvdwetering@hotmail.c University of Groningen
Wetering om
Joachim Van den
Bogaert
joachim@ccl.kuleuven.be CCL - K.U. Leuven
Antal van den Bosch Antal.vdnBosch@uvt.nl TiCC, Tilburg University
Leonoor van der Beek leonoor.vanderbeek@right
now.com
Q-go / Rightnow
Marieke van Erp Marieke@cs.vu.nl VU University Amsterdam
Frank Van Eynde frank.vaneynde@ccl.kuleu
ven.be
CCL, K.U.Leuven
Maarten van Gompel proycon@anaproy.nl TiCC, Tilburg University
Hans van Halteren hvh@let.ru.nl Radboud University
Nijmegen
Gertjan van Noord g.j.m.van.noord@rug.nl University of Groningen
Philip van Oosten philip.vanoosten@hogent.
be
LT3, University College
Ghent

LIST OF PARTICIPANTS
Menno van Zaanen mvzaanen@uvt.nl TiCC, Tilburg University
Tom Vanallemeersch tallem@ccl.kuleuven.be CCL, K.U.Leuven
Vincent Vandeghinste vincent@ccl.kuleuven.be CCL, K.U.Leuven
Klaar Vanopstal klaar.vanopstal@hogent.b LT3, University College
e
Ghent
Kateryna Vasylenko Katyaknu1986@mail.ru Nijmegen University
Peter Velaerts peter.velaerts@hogent.be LT3, University College
Ghent
Suzan Verberne s.verberne@let.ru.nl Radboud University
Nijmegen
Reinder Verlinde R.Verlinde@Elsevier.com Elsevier
yuliya vladimirova vladimirova83@gmail.com
Tim Wauters tim.wauters@intec.ugent.
be
IBBT & Ghent University
Edgar Weiffenbach s1422022@student.rug.nl CLCG, University of
Groningen
Eline Westerhout elinewesterhout@gmail.co
m
Utrecht University
Thomas Wielfaert thomas.wielfaert@ugent.b
e
Martijn Wieling m.b.wieling@rug.nl University of Groningen
Sander Wubben s.wubben@uvt.nl TiCC, Tilburg University
Jakub Zavrel zavrel@textkernel.nl Textkernel
103