Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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An important concept of LAF is that it embodies clear distinctions. Distiawan and Manurung (2012) sum this up very nicely as separating ’user format from exchange format’, ’primary data from annotation’ and ’representation from content’. In conclusion, one could also add the separation of the ’data model from its serialization’, and be prepared for more LAF/GrAF related use-cases to come. References Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman. 2007. Compilers: Principles, Techniques, and Tools (2nd Edition). Addison Wesley, August. Ann Bies, Mark Ferguson, Karen Katz, and Robert MacIntyre, 1995. Bracketing Guidelines for Treebank II Style Penn Treebank Project. Jean Carletta, Stefan Evert, Ulrich Heid, Jonathan Kilgour, Judy Robertson, and Holger Voormann. 2003. The NITE XML toolkit: Flexible annotation for multimodal language data. Behavior Research Methods, 35:353–363. Steve Cassidy. 2010. An RDF realisation of LAF in the DADA annotation server. In Proceedings of ISA-5, Hong Kong, January. Christian Chiarcos, Sebastian Nordhoff, and Sebastian Hellmann, editors. 2012. Linked Data in Linguistics. Representing and Connecting Language Data and Language Metadata. Springer, Heidelberg. Christian Chiarcos. 2012. A generic formalism to represent linguistic corpora in RDF and OWL/DL. In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey, May. Marie-Catherine de Marneffe and Christopher D. Manning. 2008. The stanford typed dependencies representation. In COLING Workshop on Crossframework and Cross-domain Parser Evaluation. Stefanie Dipper. 2005. XML-based stand-off representation and exploitation of multi-level linguistic annotation. In Berliner XML Tage, pages 39 – 50. Bayu Distiawan and Ruli Manurung. 2012. A GrAFcompliant indonesian speech recognition web service on the language grid for transcription crowdsourcing. In Proceedings of the Sixth Linguistic Annotation Workshop, pages 67–74, Jeju, Republic of Korea, July. Association for Computational Linguistics. Kerstin Eckart, Kurt Eberle, and Ulrich Heid. 2010. An infrastructure for more reliable corpus analysis. In Proceedings of the Workshop on Web Services and Processing Pipelines in HLT: Tool Evaluation, LR Production and Validation (LREC’10), pages 8– 14, Valletta, Malta, May. Kerstin Eckart, Arndt Riester, and Katrin Schweitzer. 2012. A discourse information radio news database for linguistic analysis. In Christian Chiarcos, Sebastian Nordhoff, and Sebastian Hellmann, editors, Linked Data in Linguistics. Representing and Connecting Language Data and Language Metadata, pages 65–75. Springer, Heidelberg. Boris Haselbach, Wolfgang Seeker, and Kerstin Eckart. 2012. German ”nach”-particle verbs in semantic theory and corpus data. In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey, May. Yoshihiko Hayashi, Thierry Declerck, and Chiharu Narawa. 2010. LAF/GrAF-grounded representation of dependency structures. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), Valletta, Malta, May. William J. Hutchins and Harold L. Somers. 1992. An introduction to machine translation. Academic Press. Nancy Ide and Keith Suderman. 2007. GrAF: A graph-based format for linguistic annotations. In Proceedings of the Linguistic Annotation Workshop, pages 1–8, Prague, Czech Republic, June. Association for Computational Linguistics. Nancy Ide and Keith Suderman. 2012. A model for linguistic resource description. In Proceedings of the Sixth Linguistic Annotation Workshop, pages 57–66, Jeju, Republic of Korea, July. Association for Computational Linguistics. Nancy Ide, Collin Baker, Christiane Fellbaum, and Rebecca Passonneau. 2010. The Manually Annotated Sub-Corpus: A community resource for and by the people. In Proceedings of the ACL 2010 Conference Short Papers, pages 68–73, Uppsala, Sweden, July. Association for Computational Linguistics. Esther König, Wolfgang Lezius, and Holger Voormann, 2003. TIGERSearch 2.1 User’s Manual. Chapter V - The TIGER-XML treebank encoding format. IMS, Universität Stuttgart. Manuel Kountz, Ulrich Heid, and Kerstin Eckart. 2008. A LAF/GrAF-based encoding scheme for underspecified representations of dependency structures. In Proceedings of the 6 th Conference on Language Resources and Evaluation (LREC 2008), Marrakech, Morocco, May. Taku Kudo and Yuji Matsumoto. 2002. Japanese dependency analysis using cascaded chunking. In Proceedings of CoNLL-2002, pages 63–69. Taipei, Taiwan. 514 Proceedings of KONVENS 2012 (SFLR 2012 workshop), Vienna, September 21, 2012
Jörg Mayer. 1995. Transcription of German Intonation. The Stuttgart System. ms. Stefan Rapp. 1995. Automatic phonemic transcription and linguistic annotation from known text with hidden markov models – an aligner for German. In Proceedings of ELSNET Goes East and IMACS Workshop ”Integration of Language and Speech in Academia and Industry” (Russia). Arndt Riester, David Lorenz, and Nina Seemann. 2010. A recursive annotation scheme for referential information sta tus. In Proceedings of the Seventh International Conference on Languag e Resources and Evaluation (LREC), pages 717–722, Valletta, Malta. Christian Rohrer and Martin Forst. 2006. Improving coverage and parsing quality of a large-scale LFG for German. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC), Genoa, Italy. Florian Zipser and Laurent Romary. 2010. A model oriented approach to the mapping of annotation formats using standards. In Proceedings of the Workshop on Language Resource and Language Technology Standards, LREC 2010, Malta. 515 Proceedings of KONVENS 2012 (SFLR 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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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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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
Page 463 and 464: scoring function σ : W ×L×W →
Page 465 and 466: esults in the triple 〈heutige Son
Page 467 and 468: Unlexicalized links 7,833,635 n (i)
Page 469 and 470: The difference in accuracies betwee
Page 471 and 472: Towards high-accuracy bilingual phr
Page 473 and 474: over a set of candidate translation
Page 475 and 476: each soft restriction soft m set on
Page 477 and 478: (> 98% in scalable precision) and a
Page 479 and 480: References Colin Bannard and Chris
Page 481 and 482: Preface The need for automatically
Page 483 and 484: 15:50-16:15 Coffee break 16:15-16:4
Page 485 and 486: Standards for the technical infrast
Page 487 and 488: ⎡ ⎤ phrase CATEGORY np ⎡ ⎤
Page 489 and 490: The representation of the feature v
Page 491 and 492: ement, while the interpretation as
Page 493 and 494: hand, checking the validity of FSR
Page 495 and 496: Standardizing metadata descriptions
Page 497 and 498: 2 LMF and semantic interoperability
Page 499 and 500: can register and create DCs. Users
Page 501 and 502: all kinds of lexemes, (see Cruse (1
Page 503 and 504: constraints, because many aspects o
Page 505 and 506: Menzo Windhouwer. 2012. RELcat: a R
Page 507 and 508: the meeting in Rome as a starting p
Page 509 and 510: As these documents provide the refe
Page 511 and 512: Two multi-layer annotated corpora r
Page 513: As audio data is not directly store
Page 517 and 518: Towards standardized lexical semant
Page 519: Getting involved into language reso
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