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Twitter Mentions Twitter Hashtags MNT HSH Twitter Emoticons EMO Twitter URLs URL Lengthened Words LNG 52 <strong>Proceedings</strong> of KONVENS 2012 (Main track: oral presentations), Vienna, September 19, 2012
NLP Workflow for On-line Definition Extraction from English and Slovene Text Corpora Senja Pollak 1,2 Anže Vavpetič 1,3 Janez Kranjc 1,3 1 Jožef Stefan Institute 3 International Postgraduate School Jožef Stefan Jamova 39, 1000 Ljubljana, Slovenia senja.pollak@ijs.si Nada Lavrač 1,3,4 Špela Vintar 2 2 Faculty of Arts, Aškerčeva 2, 1000 Ljubljana, Slovenia 4 University of Nova Gorica, Vipavska 13, 5000 Nova Gorica, Slovenia Abstract Definition extraction is an emerging field of NLP research. This paper presents an innovative information extraction workflow aimed to extract definition candidates from domain-specific corpora, using morphosyntactic patterns, automatic terminology recognition and semantic tagging with wordnet senses. The workflow, implemented in a novel service-oriented workflow environment ClowdFlows, was applied to the task of definition extraction from two corpora of academic papers in the domain of Computational Linguistics, one in Slovene and another in English. The definition extraction workflow is available online, therefore it can be reused for definition extraction from other corpora and is easily adaptable to other languages provided that the needed language specific workflow components were accessible as public services on the web. 1 Introduction Extracting domain-specific knowledge from texts is a challenging research task, addressed by numerous researchers in the areas of natural language processing (NLP), information extraction and text mining. Definitions of specialized concepts/terms are an important source of knowledge and an invaluable part of dictionaries, thesauri, ontologies and lexica, therefore many approaches for their extraction have been proposed by NLP researchers. For instance, Navigli and Velardi (2010), Borg et al. (2010) and Westerhout (2010) have reported very good results with nearly fully automated systems applied to English or Dutch texts. While most of the approaches follow the Aristotelian view of what constitutes a definition (X is a Y which ...), the concept of definition itself is rarely discussed in detail or given enough attention in the results interpretation. A popular way to circumvent the fuzziness of the “definition of definitions” is to label all non-ideal candidates as defining or knowledge-rich contexts to be validated by the user. In line with this philosophy, the definition extraction approach proposed in this work can be tuned in a way to ensure higher recall at a cost of lower precision. Our work is mainly focused on Slovene, a Slavic language with a very complex morphology and less fixed word order, hence the approaches developed for English and other Germanic languages, based on very large - often web-crawled - text corpora, may not be easy to adapt. In general, definition extraction systems for Slavic languages perform much worse than comparable English systems (e.g., Przepiórkowski et al. (2007), Degórski et al. (2008a, 2008b), Kobyliński and Przepiórkowski (2008)). One of the reasons is that many Slavic languages, including Slovene, lack appropriate preprocessing tools, such as parsers and chunkers, needed for the implementation of well-performing definition extraction methods. Another obstacle is the fact that very large domain corpora are rarely readily available. The main challenge addressed in this paper and the main motivation for this research is to develop a definition extraction methodology and a tool for extracting a set of candidate definition sentences from Slovene text corpora. This work follows our 53 <strong>Proceedings</strong> of KONVENS 2012 (Main track: oral presentations), Vienna, September 19, 2012
Page 1 and 2: 11 th Conference on Natural Languag
Page 3 and 4: Table of Contents Main conference P
Page 5 and 6: Comparing variety corpora with vis-
Page 7 and 8: LexSem 2012: Workshop on Recent Dev
Page 9 and 10: KONVENS 2012 The 11 th Conference o
Page 11 and 12: Organizers: Jeremy Jancsary (ÖGAI/
Page 13 and 14: Ines Rehbein (Potsdam University) M
Page 15 and 16: Integrating viewpoints into newspap
Page 17 and 18: Phonetically aided syntactic parsin
Page 19 and 20: The Statistical Approach to Natural
Page 21 and 22: Data-driven Knowledge Extraction fo
Page 23 and 24: 4 The Different Relations Types In
Page 25 and 26: Relation Type #Patterns Examples Su
Page 27 and 28: Suits-to Served-with Substituted-by
Page 29 and 30: References Timothy Chklovski and Pa
Page 31 and 32: 1.2 Problem Definitions In a MRA, t
Page 33 and 34: oot person organisation product ...
Page 35 and 36: Group Organis. Persons Products all
Page 37 and 38: Method α α+β α+γ all SentiWS 0
Page 39 and 40: A Supervised POS Tagger for Written
Page 41 and 42: Our tagset mixes and matches tags a
Page 43 and 44: ecause the enclitic pronoun is an i
Page 45 and 46: stochastic rules as opposed to larg
Page 47 and 48: Kevin Duh, and Katrin Kirchhoff. 20
Page 49 and 50: forms, and that existentials can be
Page 51: Pseudo Verb Punctuation PNC
Page 55 and 56: nological phrases on the basis of p
Page 57 and 58: Figure 2: A screenshot of the entir
Page 59 and 60: When analyzing the evaluation sets
Page 61 and 62: Projecting Semantic Roles via Tai M
Page 63 and 64: quent prediction, return it (iii) i
Page 65 and 66: for the B/T/FT settings. This is pr
Page 67 and 68: ut it is implied by Table 3 and Fig
Page 69 and 70: References Wanxiang Che, Zhenghua L
Page 71 and 72: Our ultimate goal is to develop com
Page 73 and 74: ADORNING CAUSE_FLUIDIC_MOTION COTHE
Page 75 and 76: English nouns, verbs, adjectives, a
Page 77 and 78: considerably worse than FrameNet. T
Page 79 and 80: Extraction. In Proceedings of the 1
Page 81 and 82: lexicon from a document-derived voc
Page 83 and 84: Initialize hypernym set H to contai
Page 85 and 86: ulary distance. Let d(a, b) denote
Page 87 and 88: the reduced lexicon (243) was neare
Page 89 and 90: Adding nominal spice to SALSA - fra
Page 91 and 92: SALSA verbs nouns 0 FE 0 2 1 FE 68
Page 93 and 94: gen, which in one of its meanings i
Page 95 and 96: In the next section we describe how
Page 97 and 98: Acknowledgments This work was funde
Page 99 and 100: most commonly used (Turney and Pant
Page 101 and 102: anteed because the forward feature
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5 000 feat ICA50 SVD50 SENNA Semcat
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words bicycle and tricycle go with
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of the Twelfth European Conference
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incorporate the context information
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those candidate phrases that are an
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5.2 Evaluation Metrics for WPD The
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Runs F-measures α=0.5 α=0.2 Pur.
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Evaluations (SemEval-2007), June (2
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2 Related Work The majority of pape
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and Roth, 2009; Lin and Wu, 2009).
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Features Dev Test CoNLL-2003 w 0 +
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Feature Dev Test Test+ — 93.78 91
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the Joint Conference of the 47th An
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tion. It was first included as a sp
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indicates the presence of the ellip
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[Armada britainiarrak] [Ipar Irland
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Información, (JOTRI 2003), pages 1
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Phonetically aided Syntactic Parsin
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mar network or PCFG. Grammar networ
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phonetic confusion that has the con
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in confusion in the network. This i
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Linguistic analyses of the LAST MIN
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Table 1: Comparison between corpora
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Table 2: Most frequent POS patterns
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Table 3: A problematic dialog situa
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6 Summary We have presented the cur
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S-restricted monotone alignments St
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s k n i i: f phh ohehnh ih xh s k n
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Dataset µ G µ P P [G>15] P [P >15
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Perplexity H(L | P ) 2PKE-Uni 7.002
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(1, 1) (2, 1) (3, 1) (4, 1) (1, 2)
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Use of linguistic features for impr
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whereas English has a verb-initial
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Accuracy of Accuracy of Accuracy of
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English Persian Train: Sentences 30
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to compensate for sparsity of paral
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Name # Documents # Tokens # Lemmas
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Figure 1: Precision-Recall graphs c
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A Multi-level Annotation Model for
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and sentence level with the aim of
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(Schmid, 1995; Schiller et al., 199
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EI EII Tag AKR MARK Typography ITER
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References Saima Aman and Stan Szpa
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Robust processing of noisy web-coll
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1. first strip of the papery skin o
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References Jon Chamberlain, Massimo
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ments between its LSRs. Additionall
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Figure 3: In the sense comparison v
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Using Information Retrieval Technol
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against every tree. Others avoid th
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A 0.780715 NN Figure 4: A sp
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# #Documents Index Size 1 29,704 1.
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References P. Bański, C. Belica, H
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(2012), in their corpus study of va
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DET NSUBJ ADV OBJA AMOD Seine Tante
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A Computational Semantic Analysis o
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(6) a. Is Spain very large? b. Is B
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References Alan C. Bale. 2008. A Un
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219 Proceedings of KONVENS 2012 (Ma
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221 Proceedings of KONVENS 2012 (Ma
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Using subcategorization frames to i
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5.2 Word clustering based on the Le
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erage of english grammars. In Proce
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cases not disambiguated using langu
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Not surprisingly, the results show
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Automatic Identification of Languag
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Differences also appear at the synt
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Acknowledgments The authors express
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2 Related Work Previous work on POS
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The XY tag is used for all non-word
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Comparing Variety Corpora with Vis-
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INPUT variety corpus comparability
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References Andrea Abel and Stefanie
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Features Context features (token wi
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0.7 0.7 0.6 0.6 0.5 0.5 F-measure 0
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ties into fine-grained classes. In
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verbials (e.g., therefore, still).
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word raw freq. non conn. conn. (of
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Corpus-based Acquisition of German
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Events Objects IPrec CPrec IPrec CP
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References Marco Baroni, Silvia Ber
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Preface The present proceedings con
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Workshop Program Friday, September
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Unsupervised Sentiment Analysis wit
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pling (Geman and Geman, 1984) to es
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References Anders Björkelund, Bern
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Class # Events in % neutral 45’01
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3.1.1 Rules Let t 1..n be tokens of
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Human 6 System 6 Human 4 System 4 A
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Codebuch Akteursanalyse. rev. 2011.
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of our work in the whole applicatio
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3.4 Negation and graduation Valence
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local context. For instance, in une
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n’était pas très favorable au G
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Domain-specific variation of sentim
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query building blocks comments real
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References Monica Bednarek. 2006. E
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3 Basic Methodology of Annotation T
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(3) A. V případě jeho kandidatur
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4.3 Auxiliary data - Mall.cz We hav
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Overlap f-score Source 0.750 Polar
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A Phrase-Based Opinion List for the
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the Stuttgart-Tübingen Tagset (STT
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As for noun-based opinion phrases a
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More than half of the words and phr
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treebank. In Proceedings of the Wor
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Apart from these, we distinguish op
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close variants)”. In fact importa
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What is the Meaning of 5 *’s? An
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For each category, we follow (Const
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Positivity Term 2.87692577130 elske
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In recent years, the use of rating
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KONVENS 2012 LThist 2012: First Int
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Organisation Committee: Thierry Dec
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Page 333 and 334:
Rule-Based Normalisation of Histori
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isation rules were applied to the i
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ments in the gold standard have bee
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cation in the contemporary SUC samp
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proach. References Maria Ågren, Ro
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2 Related Work There has been consi
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vided to ensure that they are norma
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alects, or even languages. Norma ca
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ization (rather than modernization)
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The Sketch Engine as infrastructure
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2.1 The Corpus of English Dialogues
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Other notable contrasts are that 17
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Evaluating a Post-editing Approach
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Figure 1: Example of a marriage lic
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all possible concatenations of indi
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Table 3: Post-editing results. Time
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okstaffua, bokstaffwa, bokstafwa, b
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Rule Cost v↔u 0.1 v↔w 0.1 u↔o
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graphic word boundaries and conside
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ation. Gulordava and Baroni (2011),
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ut adding a statistical measure to
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e.g. philosophical and political pr
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of the EACL 2009 Workshop on GEMS:
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Most of the research on genre class
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Precision Recall F 1 Unigrams 0.551
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The DTA ‘base format’: A TEI-Su
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Lib are complementary in the sense
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inline metadata solutions for edito
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ing. The fourth level contains an e
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the work package ‘Resources and T
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is mainly used with parallel corpor
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Corrections by the editor were omit
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Accuracy N % All Words 781/1000 78.
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$$ $$ $$ $$ $$ $$ $$ $$ $$ Figure 4
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Digitised Historical Text: Does it
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3 Related Work Previous research on
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5.1 End-of-line Soft Hyphen Deletio
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quite difficult to get an idea of h
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References Ahmad Abdulkader and Mat
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cision, recall, and F1. Section 4 d
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not corrected corrected p P R F1 P
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From Semi-Automatic to Automatic Af
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Figure 2: ER-Diagramm of the Morphi
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Figure 4: Morphilizer’s implement
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Figure 6: Architecture of Morphorm
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References Harald R. Baayen, R. Pie
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edition has been adopted. This is d
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marginalia, emendations or punctuat
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Word Lemma Class Subclass Type Tens
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Figure 3. Sample screenshot of comp
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KONVENS 2012 LexSem 2012: Workshop
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English) closely and discusses the
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verbs were collected mostly from an
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3 Theoretical Background Let us now
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are sensible to semantic properties
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Cançado, Márcia and Amaral, Luana
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morphological tagger for Russian la
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whom/which something is taken. (7cz
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Verb class same frame different fra
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Construction Grammar are introduced
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posed to Hon åker på måndag ‘S
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nitions of Agent, for example. Inst
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Framenet++, VR dnr 2010-6013), by t
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Källström and Inger Lindberg, edi
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scoring function σ : W ×L×W →
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esults in the triple 〈heutige Son
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Unlexicalized links 7,833,635 n (i)
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The difference in accuracies betwee
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Towards high-accuracy bilingual phr
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over a set of candidate translation
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each soft restriction soft m set on
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(> 98% in scalable precision) and a
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References Colin Bannard and Chris
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Preface The need for automatically
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15:50-16:15 Coffee break 16:15-16:4
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Standards for the technical infrast
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⎡ ⎤ phrase CATEGORY np ⎡ ⎤
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The representation of the feature v
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ement, while the interpretation as
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hand, checking the validity of FSR
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Standardizing metadata descriptions
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2 LMF and semantic interoperability
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can register and create DCs. Users
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all kinds of lexemes, (see Cruse (1
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constraints, because many aspects o
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Menzo Windhouwer. 2012. RELcat: a R
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the meeting in Rome as a starting p
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As these documents provide the refe
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Two multi-layer annotated corpora r
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As audio data is not directly store
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Jörg Mayer. 1995. Transcription of
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Towards standardized lexical semant
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Getting involved into language reso
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