Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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14:30–15:15 Third session: OCR Digitised historical text: Does it have to be mediOCRe? Bea Alex, Claire Grover, Ewan Klein and Richard Tobin Comparison of named entity extraction tools for raw OCR text Kepa Joseba Rodriquez, Michael Bryant, Tobias Blanke and Magdalena Luszczynska 15:15–15:45 Tea/coffee break 15:45–16:30 Fourth session: Middle English From semi-automatic to automatic affix extraction in Middle English corpora: Building a sustainable database for analyzing derivational morphology over time Hagen Peukert The reference corpus of Late Middle English scientific prose Javier Calle-Martín, Laura Esteban-Segura, Teresa Marqués-Aguado and Antonio Miranda-García 16:30–17:15 Panel discussion: Lexicon (Chair: Brigitte Krenn) Panelists: Karlheinz Mörth, Hélène Pignot, Adam Kilgariff, Yvonne Adesam and Thierry Declerck 17:15–17:30 Creative break 17:30–17:45 Networking 17:45–18:00 Closing session 332
Rule-Based Normalisation of Historical Text – a Diachronic Study Eva Pettersson † , Beáta Megyesi and Joakim Nivre Department of Linguistics and Philology Uppsala University † Swedish National Graduate School of Language Technology firstname.lastname@lingfil.uu.se Abstract Language technology tools can be very useful for making information concealed in historical documents more easily accessible to historians, linguists and other researchers in humanities. For many languages, there is however a lack of linguistically annotated historical data that could be used for training NLP tools adapted to historical text. One way of avoiding the data sparseness problem in this context is to normalise the input text to a more modern spelling, before applying NLP tools trained on contemporary corpora. In this paper, we explore the impact of a set of hand-crafted normalisation rules on Swedish texts ranging from 1527 to 1812. Normalisation accuracy as well as tagging and parsing performance are evaluated. We show that, even though the rules were generated on the basis of one 17th century text sample, the rules are applicable to all texts, regardless of time period and text genre. This clearly indicates that spelling correction is a useful strategy for applying contemporary NLP tools to historical text. 1 Introduction Digitalised historical text is a rich source of information for researchers in humanities. However, there is a lack of language technology tools adapted to old texts, that could make the information more easily accessible. As an example, the work presented in this paper has been carried out in close cooperation with historians in the Gender and Work project (Ågren et al., 2011). This project aims at exploring how men and women supported themselves in the Early Modern Swedish period (1550–1800). Information on this is manually searched for in documents from this period, and stored in a database. The project has shown that work activities are most often described as a verb and its complements. Automatically extracting all the verbs in a text would be a rather trivial task for contemporary text, using standard NLP tokenisation and tagging. However, for historians and other researchers in humanities, the manual way of searching for information is still often the only alternative. Developing NLP tools for historical text is a great challenge, since old texts vary greatly in both spelling and grammar between different authors, genres and time periods, and even within the same text, due to the lack of spelling conventions. The texts that we have studied are generally written in a spoken language fashion, with less distinct boundaries between sentences, and a spelling that to a larger extent reflects the phonetic form of the word. Sentences with 50–100 words or more are not unusual. The Swedish language was also strongly influenced by other languages at this time. Evidence of this is the placement of the finite verb at the end of subordinate clauses in a German-like style not usually found in modern Swedish texts. Another problem in developing language technology tools specifically aimed at handling historical text, is the shortage of linguistically annotated historical data. One way of getting around this problem is to use existing NLP tools trained for handling contemporary text, and normalise the historical input text to a more modern spelling be- 333 <strong>Proceedings</strong> of KONVENS 2012 (LThist 2012 workshop), Vienna, September 21, 2012
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11 th Conference on Natural Languag
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Table of Contents Main conference P
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Comparing variety corpora with vis-
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LexSem 2012: Workshop on Recent Dev
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KONVENS 2012 The 11 th Conference o
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Organizers: Jeremy Jancsary (ÖGAI/
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Ines Rehbein (Potsdam University) M
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Integrating viewpoints into newspap
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Phonetically aided syntactic parsin
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The Statistical Approach to Natural
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Data-driven Knowledge Extraction fo
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4 The Different Relations Types In
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Relation Type #Patterns Examples Su
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Suits-to Served-with Substituted-by
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References Timothy Chklovski and Pa
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1.2 Problem Definitions In a MRA, t
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oot person organisation product ...
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Group Organis. Persons Products all
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Method α α+β α+γ all SentiWS 0
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A Supervised POS Tagger for Written
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Our tagset mixes and matches tags a
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ecause the enclitic pronoun is an i
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stochastic rules as opposed to larg
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Kevin Duh, and Katrin Kirchhoff. 20
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forms, and that existentials can be
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Pseudo Verb Punctuation PNC
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NLP Workflow for On-line Definition
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nological phrases on the basis of p
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Figure 2: A screenshot of the entir
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When analyzing the evaluation sets
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Projecting Semantic Roles via Tai M
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quent prediction, return it (iii) i
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for the B/T/FT settings. This is pr
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ut it is implied by Table 3 and Fig
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References Wanxiang Che, Zhenghua L
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Our ultimate goal is to develop com
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ADORNING CAUSE_FLUIDIC_MOTION COTHE
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English nouns, verbs, adjectives, a
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considerably worse than FrameNet. T
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Extraction. In Proceedings of the 1
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lexicon from a document-derived voc
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Initialize hypernym set H to contai
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ulary distance. Let d(a, b) denote
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the reduced lexicon (243) was neare
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Adding nominal spice to SALSA - fra
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SALSA verbs nouns 0 FE 0 2 1 FE 68
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gen, which in one of its meanings i
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In the next section we describe how
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Acknowledgments This work was funde
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most commonly used (Turney and Pant
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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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Page 295 and 296: References Monica Bednarek. 2006. E
Page 297 and 298: 3 Basic Methodology of Annotation T
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Page 315 and 316: Apart from these, we distinguish op
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Page 319 and 320: What is the Meaning of 5 *’s? An
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Page 327 and 328: KONVENS 2012 LThist 2012: First Int
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Page 335 and 336: isation rules were applied to the i
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Page 343 and 344: 2 Related Work There has been consi
Page 345 and 346: vided to ensure that they are norma
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Page 353 and 354: 2.1 The Corpus of English Dialogues
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Page 357 and 358: Evaluating a Post-editing Approach
Page 359 and 360: Figure 1: Example of a marriage lic
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Page 363 and 364: Table 3: Post-editing results. Time
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Page 367 and 368: Rule Cost v↔u 0.1 v↔w 0.1 u↔o
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Page 377 and 378: of the EACL 2009 Workshop on GEMS:
Page 379 and 380: Most of the research on genre class
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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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Workshop Program Friday, September
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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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