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Scalable precision Rank \ Size 2 3 4 5 6 7 8 9 10 11 12 all 1-500 1.00/1.00 - 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 0.95/0.95 1.00-1.00 10001-10500 1.00/1.00 0.80/0.80 0.99/0.99 0.96/0.97 0.99/1.00 0.87/0.92 0.98/0.99 0.96/0.97 0.99/0.99 1.00/1.00 0.98/0.98 0.98-0.99 20001-20500 0.96/0.98 0.92/0.87 0.97/0.99 0.96/0.96 0.96/0.98 0.92/0.92 0.98/0.98 0.93/0.96 0.97/0.99 0.93/0.95 0.93/0.96 0.96-0.98 30001-30500 0.97/0.98 0.84/0.86 0.98/0.98 0.91/0.95 0.92/0.98 0.88/0.92 0.88/0.98 0.89/0.95 0.97/0.99 - 0.83/0.96 0.97-0.98 40001-40500 0.83/0.92 0.85/0.85 0.83/0.96 0.77/0.87 0.88/0.97 0.75/0.86 0.89/0.97 0.91/0.94 0.85/0.99 0.91/0.95 0.25/0.95 0.83-0.95 50001-50500 0.16/0.55 0.15/0.49 0.16/0.93 0.40/0.85 0.47/0.96 0.33/0.84 0.38/0.97 0.44/0.93 - - - 0.17-0.82 Strict precision, thresh = (0 * size) / 12 Rank \ Size 2 3 4 5 6 7 8 9 10 11 12 all 1-500 1.00/1.00 - 0.99/0.99 1.00/1.00 0.99/0.99 1.00/1.00 1.00/1.00 1.00/1.00 0.95/0.95 1.00/1.00 0.43/0.43 0.98-0.98 10001-10500 1.00/1.00 0.40/0.40 0.97/0.99 0.82/0.88 0.97/0.98 0.42/0.65 0.93/0.96 0.71/0.81 0.95/0.95 1.00/1.00 0.85/0.76 0.92-0.95 20001-20500 0.95/0.98 0.75/0.62 0.92/0.96 0.78/0.82 0.85/0.94 0.56/0.61 0.88/0.93 0.43/0.66 0.80/0.93 0.68/0.76 0.50/0.68 0.84-0.91 30001-30500 0.96/0.97 0.53/0.57 0.95/0.96 0.65/0.77 0.67/0.92 0.33/0.57 0.75/0.93 0.50/0.65 0.67/0.93 - 0.00/0.67 0.90-0.91 40001-40500 0.79/0.90 0.68/0.65 0.66/0.92 0.33/0.58 0.57/0.87 0.23/0.45 0.45/0.88 0.46/0.60 0.25/0.92 0.00/0.71 0.00/0.65 0.62-0.85 50001-50500 0.14/0.54 0.12/0.38 0.07/0.88 0.40/0.57 0.33/0.86 0.00/0.43 0.00/0.87 0.00/0.59 - - - 0.13-0.73 Strict precision, thresh = (1 * size) / 12 Rank \ Size 2 3 4 5 6 7 8 9 10 11 12 all 1-500 1.00/1.00 - 0.99/0.99 1.00/1.00 0.99/0.99 1.00/1.00 1.00/1.00 1.00/1.00 0.95/0.95 1.00/1.00 1.00/1.00 0.99-0.99 10001-10500 1.00/1.00 0.40/0.40 0.97/0.99 0.82/0.88 0.97/0.98 0.42/0.65 0.93/0.96 0.71/0.81 0.95/0.95 1.00/1.00 0.96/0.97 0.93-0.96 20001-20500 0.95/0.98 0.75/0.62 0.92/0.96 0.78/0.82 0.85/0.94 0.56/0.61 0.88/0.93 0.43/0.66 0.80/0.93 0.68/0.76 0.79/0.91 0.84-0.92 30001-30500 0.96/0.97 0.53/0.57 0.95/0.96 0.65/0.77 0.67/0.92 0.33/0.57 0.75/0.93 0.50/0.65 0.67/0.93 - 0.00/0.90 0.90-0.92 40001-40500 0.79/0.90 0.68/0.65 0.66/0.92 0.33/0.58 0.57/0.87 0.23/0.45 0.45/0.88 0.46/0.60 0.25/0.92 0.00/0.71 0.00/0.88 0.62-0.86 50001-50500 0.14/0.54 0.12/0.38 0.07/0.88 0.40/0.57 0.33/0.86 0.00/0.43 0.00/0.87 0.00/0.59 - - - 0.13-0.74 Strict precision, thresh = (2 * size) / 12 Rank \ Size 2 3 4 5 6 7 8 9 10 11 12 all 1-500 1.00/1.00 - 0.99/0.99 1.00/1.00 0.99/0.99 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00-1.00 10001-10500 1.00/1.00 0.40/0.40 0.97/0.99 0.82/0.88 0.99/0.99 0.67/0.80 0.96/0.98 0.93/0.95 0.99/0.99 1.00/1.00 1.00/1.00 0.96-0.98 20001-20500 0.95/0.98 0.75/0.62 0.92/0.96 0.78/0.82 0.93/0.97 0.89/0.84 0.95/0.97 0.93/0.94 0.92/0.98 0.88/0.91 0.93/0.98 0.92-0.96 30001-30500 0.96/0.97 0.53/0.57 0.95/0.96 0.65/0.77 0.86/0.96 0.83/0.84 0.75/0.96 0.50/0.92 1.00/0.98 - 1.00/0.98 0.92-0.95 40001-40500 0.79/0.90 0.68/0.65 0.66/0.92 0.33/0.58 0.78/0.94 0.68/0.79 0.73/0.94 0.85/0.90 0.25/0.97 1.00/0.92 0.00/0.96 0.69-0.90 50001-50500 0.14/0.54 0.12/0.38 0.07/0.88 0.40/0.57 0.33/0.93 0.00/0.75 0.00/0.93 0.00/0.88 - - - 0.13-0.77 Strict precision, thresh = (3 * size) / 12 Rank \ Size 2 3 4 5 6 7 8 9 10 11 12 all 1-500 1.00/1.00 - 0.99/0.99 1.00/1.00 0.99/0.99 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00-1.00 10001-10500 1.00/1.00 0.40/0.40 0.99/0.99 1.00/1.00 0.99/0.99 0.67/0.80 0.96/0.98 1.00/1.00 0.99/0.99 1.00/1.00 1.00/1.00 0.97-0.98 20001-20500 0.95/0.98 0.75/0.62 0.97/0.98 1.00/1.00 0.93/0.97 0.89/0.84 0.98/0.98 1.00/1.00 0.96/0.99 0.94/0.96 1.00/1.00 0.95-0.97 30001-30500 0.96/0.97 0.53/0.57 0.97/0.98 0.94/0.98 0.86/0.96 0.83/0.84 0.75/0.97 1.00/1.00 1.00/0.99 - 1.00/1.00 0.94-0.97 40001-40500 0.79/0.90 0.68/0.65 0.79/0.95 0.79/0.90 0.78/0.94 0.68/0.79 0.95/0.97 0.85/0.96 1.00/0.99 1.00/0.96 0.00/0.98 0.78-0.93 50001-50500 0.14/0.54 0.12/0.38 0.07/0.91 0.40/0.87 0.33/0.93 0.00/0.75 0.50/0.97 0.00/0.94 - - - 0.14-0.80 Strict precision, thresh = (4 * size) / 12 Rank \ Size 2 3 4 5 6 7 8 9 10 11 12 all 1-500 1.00/1.00 - 0.99/0.99 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 1.00-1.00 10001-10500 1.00/1.00 1.00/1.00 0.99/0.99 1.00/1.00 1.00/1.00 1.00/1.00 0.96/0.98 1.00/1.00 1.00/1.00 1.00/1.00 1.00/1.00 0.99-0.99 20001-20500 0.95/0.98 1.00/1.00 0.97/0.98 1.00/1.00 0.98/0.99 1.00/1.00 0.98/0.98 1.00/1.00 1.00/1.00 0.97/0.98 1.00/1.00 0.98-0.99 30001-30500 0.96/0.97 1.00/1.00 0.97/0.98 0.94/0.98 1.00/0.99 1.00/1.00 0.75/0.97 1.00/1.00 1.00/1.00 - 1.00/1.00 0.97-0.98 40001-40500 0.79/0.90 0.93/0.95 0.79/0.95 0.79/0.90 0.96/0.99 0.77/0.92 0.95/0.97 1.00/1.00 1.00/1.00 1.00/0.98 0.00/0.98 0.84-0.96 50001-50500 0.14/0.54 0.13/0.53 0.07/0.91 0.40/0.87 0.33/0.98 0.00/0.88 0.50/0.97 0.00/0.98 - - - 0.14-0.82 Table 2: Precision depending on size, rank and evaluation criteria. Rank / Size 2 3 4 5 6 7 8 9 10 11 12 0-9999 934-9% 45-0% 1928-19% 218-2% 1764-18% 485-5% 1340-13% 699-7% 977-10% 1031-10% 579-6% 10000-19999 682-7% 100-1% 1662-17% 307-3% 1649-16% 447-4% 1947-19% 629-6% 1299-13% 611-6% 667-7% 20000-29999 1169-12% 243-2% 2969-30% 699-7% 2416-24% 666-7% 748-7% 289-3% 360-4% 265-3% 176-2% 30000-39999 2223-22% 787-8% 3082-31% 948-9% 1160-12% 571-6% 489-5% 303-3% 260-3% 71-1% 106-1% 40000-49999 4191-42% 1545-15% 1690-17% 689-7% 636-6% 375-4% 369-4% 189-2% 194-2% 45-0% 77-1% Total 15288-27% 3830-7% 11449-20% 2879-5% 7650-13% 2557-4% 4906-9% 2117-4% 3097-5% 2025-4% 1608-3% Table 3: Number and distribution over size of the translation hypotheses generated. 478 Proceedings of KONVENS 2012 (LexSem 2012 workshop), Vienna, September 21, 2012
References Colin Bannard and Chris Callison-Burch. 2005. Paraphrasing with bilingual parallel corpora. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL ’05, pages 597–604, Stroudsburg, PA, USA. Association for Computational Linguistics. Y. Deng and W. Byrne. 2008. Hmm word and phrase alignment for statistical machine translation. Audio, Speech, and Language Processing, IEEE Transactions on, 16(3):494–507. William A. Gale and Kenneth W. Church. 1991. Identifying word correspondence in parallel texts. In Proceedings of the workshop on Speech and Natural Language, HLT ’91, pages 152–157, Stroudsburg, PA, USA. Association for Computational Linguistics. Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003. Statistical phrase-based translation. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1, NAACL ’03, pages 48–54, Stroudsburg, PA, USA. Association for Computational Linguistics. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, and Evan Herbst. 2007. Moses: open source toolkit for statistical machine translation. In Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, ACL ’07, pages 177–180, Stroudsburg, PA, USA. Association for Computational Linguistics. Caroline Lavecchia, David Langlois, and Kamel Smaïli. 2008. Phrase-Based Machine Translation based on Simulated Annealing. In European Language Resources Association (ELRA), editor, Sixth international conference on Language Resources and Evaluation - LREC 2008, Marrakech, Maroc. I. Dan Melamed. 1995. Automatic evaluation and uniform filter cascades for inducing n-best translation lexicons. In In Proceedings of the Third Workshop on Very Large Corpora, pages 184–198. I. Dan Melamed. 1997. Automatic discovery of noncompositional compounds in parallel data. In Proceedings of the 2nd Conference on Empirical Methods in Natural Language Processing. I. Dan Melamed. 2000. Models of translational equivalence among words. Computational Linguistics, 26:221–249. Robert C. Moore. 2001. Towards a simple and accurate statistical approach to learning translation relationships among words. In Proceedings of the workshop on Data-driven methods in machine translation - Volume 14, DMMT ’01, pages 1–8, Stroudsburg, PA, USA. Association for Computational Linguistics. H.T. Ng, B. Wang, and Y.S. Chan. 2003. Exploiting parallel texts for word sense disambiguation: An empirical study. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1, pages 455–462. Association for Computational Linguistics. M. Sahlgren and J. Karlgren. 2005. Automatic bilingual lexicon acquisition using random indexing of parallel corpora. Nat. Lang. Eng., 11(3):327–341, September. O. Streiter, M. Stuflesser, and I. Ties. 2004. Cle, an aligned tri-lingual ladin-italian-german corpus. corpus design and interface. First Steps in Language Documentation for Minority Languages, page 84. Keita Tsuji and Kyo Kageura. 2004. Extracting lowfrequency translation pairs from japanese-english bilingual corpora. In Sophia Ananadiou and Pierre Zweigenbaum, editors, COLING 2004 CompuTerm 2004: 3rd International Workshop on Computational Terminology, pages 23–30, Geneva, Switzerland, August 29. COLING. Dan Tufis. 2002. A cheap and fast way to build useful translation lexicons. In in Proceedings of the 19th International Conference on Computational Linguistics, COLING2002, pages 1030–1036. Dominic Widdows, Beate Dorow, and Chiu ki Chan. 2002. Using parallel corpora to enrich multilingual lexical resources. In In Third International Conference on Language Resources and Evaluation, pages 240–245. Dekai Wu and Xuanyin Xia. 1994. Learning an english-chinese lexicon from a parallel corpus. In In Proceedings of the First Conference of the Association for Machine Translation in the Americas, pages 206–213. Ying Zhang and Stephan Vogel. 2005. An efficient phrase-to-phrase alignment model for arbitrarily long phrase and large corpora. In In Proceedings of the 10th Conference of the European Association for Machine Translation (EAMT-05, pages 30–31. 479 Proceedings of KONVENS 2012 (LexSem 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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Workshop Program Friday, September
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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
Page 391 and 392:
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
Page 409 and 410:
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
Page 427 and 428: marginalia, emendations or punctuat
Page 429 and 430: Word Lemma Class Subclass Type Tens
Page 431 and 432: Figure 3. Sample screenshot of comp
Page 433 and 434: KONVENS 2012 LexSem 2012: Workshop
Page 435 and 436: English) closely and discusses the
Page 437 and 438: Workshop Program Friday, September
Page 439 and 440: verbs were collected mostly from an
Page 441 and 442: 3 Theoretical Background Let us now
Page 443 and 444: are sensible to semantic properties
Page 445 and 446: Cançado, Márcia and Amaral, Luana
Page 447 and 448: morphological tagger for Russian la
Page 449 and 450: whom/which something is taken. (7cz
Page 451 and 452: Verb class same frame different fra
Page 453 and 454: Construction Grammar are introduced
Page 455 and 456: posed to Hon åker på måndag ‘S
Page 457 and 458: nitions of Agent, for example. Inst
Page 459 and 460: Framenet++, VR dnr 2010-6013), by t
Page 461 and 462: 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: (> 98% in scalable precision) and a
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 and 514: As audio data is not directly store
Page 515 and 516: Jörg Mayer. 1995. Transcription of
Page 517 and 518: Towards standardized lexical semant
Page 519: Getting involved into language reso
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Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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