Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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Preface The interaction between Human Language Technology (HLT) and Digital Humanities (DH) at large has been of interest in various projects and initiatives during the last years, aiming to bring forward language resources and tools for the Humanities, Social Sciences and Cultural Heritage. The specific focus of LThist 2012 lies on the development of technology and resources required for processing historical texts. Workshop contributors and participants discuss ways and strategies for shaping HLT resources (tools, data and metadata) in ways that are maximally beneficial for researchers in the Humanities. The necessity for a strong interplay between proponents from language technology and from the Humanities is also reflected in the invited talks. While Caroline Sporleder takes a language technology perspective, Sonia Horn addresses the needs and requirements from a medical historian's point of view. A major aspect of the workshop is the exchange of experiences with and comparison of tools, approaches, and standards that make historical texts accessible to automatic processing. Moreover, LThist encourages the interchange of historical data and processing tools. In the present workshop, historical texts are understood in two ways: i) texts as documents of older forms of languages, and ii) texts as documentations of historical content. Accordingly, the contributions comprise a broad range of topics, genres and diachronic language varieties, including scientific prose, narratives, folk tales, riddles etc., as well as trade-related documents and marriage license books with the latter being are valuable resource for demography studies. The presented papers address various aspects of data preparation and (semi-)automatic processing for a number of languages including Old Swedish, Late Middle English, Middle English, Early Modern English and Modern English, diachronic varieties of German, Dutch and Spanish, and Old Occitan. The proposed approaches and technical solutions center around problem areas such as improving the OCR quality of historical texts, orthography harmonization and mapping historical to modern word forms, as prerequisites for automatic mining of historical texts. Also, the possibilities of cross-language transfer of morphosyntactic and syntactic annotation from resource-rich source languages to underresourced target languages are examined. Technical infrastructures, specifically tailored for historical corpora, are discussed, including mark-up languages for historical texts and representation formats for diachronic lexical databases, processing tools and architectures. Overall, LThist 2012 well reflects the current discussions regarding automatic processing of historical texts where OCR errors and the lack of harmonization in orthography are still major practical issues, but where also machine learning and cross-language transfer are coming more and more into focus. Thierry Declerck, Brigitte Krenn and Karlheinz Mörth Workshop Organizers Saarbrücken & Vienna, September 2012 328
Organisation Committee: Thierry Declerck (DFKI GmbH, Saarbrücken, Germany & ICLTT-ÖAW, Vienna, Austria) Brigitte Krenn (ÖFAI, Vienna, Austria) Karlheinz Mörth (ICLTT-ÖAW, Vienna, Austria) Program Committee: Ulrike Czeitschner (Austrian Academy of Sciences) Thierry Declerck (DFKI GmbH & Austrian Academy of Sciences) Stefanie Dipper (University of Bochum) Martin Durrell (University of Manchester) Anette Frank (University of Heidelberg) Jost Gippert (University of Francfort) Vangelis Karkaletsis (NCSR "Demokritos") Brigitte Krenn (ÖFAI, Vienna) Anastasia Krithara (NCSR "Demokritos") Piroska Lendvai (Hungarian Academy of Sciences) Anke Lüdeling (Humboldt University, Berlin) Karlheinz Mörth (Austrian Academy of Sciences) Hélène Pignot (Université Paris I) Michael Piotrowski (University of Zürich) Claudia Resch (Austrian Academy of Sciences) Silke Scheible (University of Stuttgart) Eiríkur Rögnvaldsson (University of Iceland Antal van den Bosch (Radbout University Nijmegen) Martin Volk (University of Zürich) Kalliopi Zervanou (University of Tilburg) Invited Speakers: Caroline Sporleder (Saarland University) Sonia Horn (Medical University of Vienna) Sponsors: We are very grateful to the AMICUS (Automated Motif Discovery in Cultural Heritage and Scientific Communication Texts) Network for funding the invited talk by Caroline Sporleder: http://ilk.uvt.nl/amicus 329
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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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Page 279 and 280: Human 6 System 6 Human 4 System 4 A
Page 281 and 282: Codebuch Akteursanalyse. rev. 2011.
Page 283 and 284: of our work in the whole applicatio
Page 285 and 286: 3.4 Negation and graduation Valence
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Page 291 and 292: Domain-specific variation of sentim
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Page 295 and 296: References Monica Bednarek. 2006. E
Page 297 and 298: 3 Basic Methodology of Annotation T
Page 299 and 300: (3) A. V případě jeho kandidatur
Page 301 and 302: 4.3 Auxiliary data - Mall.cz We hav
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Page 305 and 306: A Phrase-Based Opinion List for the
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Page 313 and 314: treebank. In Proceedings of the Wor
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
Page 321 and 322: For each category, we follow (Const
Page 323 and 324: Positivity Term 2.87692577130 elske
Page 325 and 326: In recent years, the use of rating
Page 327: KONVENS 2012 LThist 2012: First Int
Page 331 and 332: Workshop Program Friday, September
Page 333 and 334: Rule-Based Normalisation of Histori
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Page 337 and 338: ments in the gold standard have bee
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Page 343 and 344: 2 Related Work There has been consi
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Page 351 and 352: The Sketch Engine as infrastructure
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
Page 365 and 366: okstaffua, bokstaffwa, bokstafwa, b
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:
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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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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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