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stochastic context-free grammars. Technical report, Swiss Federal Institute of T echnology. J.-C. Chappelier, M. Rajman, R. Arages, and A. Rozenknop. 1999. Lattice parsing for speech recognition. In Proceedings of 6th Conference on Traitement Automata du Langage Naturel TALN, pages 95–104. Eugene Charniak. 2001. Immediate-head parsing for language models. In Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, ACL ’01, pages 124–131, Stroudsburg, PA, USA. Association for Computational Linguistics. Ciprian Chelba and Frederick Jelinek. 1998. Exploiting syntactic structure for language modeling. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, ACL ’98, pages 225–231, Stroudsburg, PA, USA. Association for Computational Linguistics. Ciprian Chelba. 2000. Exploiting Syntactic Structure for Natural Language Modeling. Ph.D. thesis, Johns Hopkins University. Christopher Collins, Bob Carpenter, and Gerald Penn. 2004. Head-driven parsing for word lattices. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, ACL ’04, Stroudsburg, PA, USA. Association for Computational Linguistics. Erik D. Demaine, Shay Mozes, Benjamin Rossman, and Oren Weimann. 2009. An optimal decomposition algorithm for tree edit distance. ACM Trans. Algorithms, 6(1):2:1–2:19, dec. Chris Dyer, Adam Lopez, Juri Ganitkevitch, Johnathan Weese, Ferhan Ture, Phil Blunsom, Hendra Setiawan, Vladimir Eidelman, and Philip Resnik. 2010. cdec: A decoder, alignment, and learning framework for finite-state and context-free translation models. In Annual Meeting of the Association for Computational Linguistics (ACL). Yoav Goldberg and Michael Elhadad. 2011. Joint hebrew segmentation and parsing using a pcfg-la lattice parser. In Proceedings of the 49th Annual Meeting of the ACL, Stroudsburg, PA, USA. Keith Hall and Mark Johnson. 2003. Language modeling using efficient best-first bottom-up parsing. In In Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop. Keith Hall and Mark Johnson. 2004. Attention shifting for parsing speech. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Stroudsburg, PA, USA. Association for Computational Linguistics. Beth A. Hockey and Manny Rayner. 2005. Comparison of grammar based and statistical language models trained on the same data. In Proceedings of the AAAI Workshop on SLU, Pittsburgh, PA, July. Jie Jiang, Zeeshan Ahmed, Julie Carson-Berndsen, Peter Cahill, and Andy Way. 2011. Phonetic representation-based speech translation. In 13th Machine Translation Summit, Xiamen , China. Dan Klein and Christopher D. Manning. 2003. Accurate unlexicalized parsing. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1, pages 423–430, Stroudsburg, PA, USA. Association for Computational Linguistics. Selçuk Köprü and Adnan Yazici. 2009. Lattice parsing to integrate speech recognition and rule-based machine translation. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pages 469–477, Stroudsburg, PA, USA. Association for Computational Linguistics. Brian Roark, Mary Harper, Eugene Charniak, Bonnie Dorr, Mark Johnson, Jeremy G. Kahn, Yang Liu, Mari Ostendorf, John Hale, Anna Krasnyanskaya, Matthew Lease, Izhak Shafran, Matthew Snover, Robin Stewart, and Lisa Yung. 2006. Sparseval: Evaluation metrics for parsing speech. In Language Resources and Evaluation (LREC), Genoa, Italy. Brian Edward Roark. 2001. Robust probabilistic predictive syntactic processing: motivations, models, and applications. Ph.D. thesis, Providence, RI, USA. Andreas Stolcke. 2002. SRILM - An Extensible Language Modeling Toolkit. In International Conference on Spoken Language Processing, Denver, Colorado. Masaru Tomita. 1985. An efficient context-free parsing algorithm for natural languages. In Proceedings of the 9th international joint conference on Artificial intelligence, pages 756–764, San Francisco, USA. Jonathan Weese, Juri Ganitkevitch, Chris Callison- Burch, Matt Post, and Adam Lopez. 2011. Joshua 3.0: Syntax-based machine translation with the thrax grammar extractor. In 6th Workshop on Statistical Machine Translation, pages 478–484, Edinburgh, Scotland. 144 Proceedings of KONVENS 2012 (Main track: oral presentations), Vienna, September 20, 2012
Linguistic analyses of the LAST MINUTE corpus Dietmar Rösner, Manuela Kunze, Mirko Otto Otto-von-Guericke Universität, Institut für Wissensund Sprachverarbeitung Fakultät für Informatik Postfach 4120, D-39016 Magdeburg roesner@ovgu.de Jörg Frommer Otto-von-Guericke-Universität, Universitätsklinik für Psychosomatische Medizin und Psychotherapie Leipziger Straße 44,D-39120 Magdeburg joerg.frommer@med.ovgu.de Abstract The LAST MINUTE corpus comprises multimodal records from a Wizard of Oz (WoZ) experiment with naturalistic dialogs between users and a simulated companion system. We report about analysing the transcripts of the user companion dialogs and about insights gained so far from this ongoing empirical research. 1 Introduction "Really natural language processing" (Cowie and Schröder, 2005), i.e. the possibility that human users speak to machines just as they would speak to another person, is a prerequisite for many future applications and devices. It is especially essential for so called companion systems (Wilks, 2010). Corpora with naturalistic data from either human to human (e.g. (Oertel et al., 2012)) or human-machine interactions (e.g. (Legát et al., 2008), (Webb et al., 2010)) are an essential resource for research in this area. In the following we report about ongoing work in the linguistic analysis of the transcripts from the LAST MINUTE corpus. This corpus comprises multimodal records from a Wizard of Oz (WoZ) experiment with naturalistic dialogs between users and a simulated companion system. The paper is organized as follows: In section 2 we give a short overview of the WoZ experiments. This is followed by a description of the LAST MINUTE corpus in section 3. In section 4 we report about analyses and empirical investigations with the transcripts. We sum up with a discussion of ongoing and future work. 2 The WoZ experiments 2.1 Design issues Our WoZ-scenario is designed in such a way that many aspects of user companion interaction (UCI) that are relevant in mundane situations of planning, re-planning and strategy change (e.g. conflicting goals, time pressure, ...) will be experienced by the subjects (Rösner et al., 2011) . The overall structure of an experiment is divided into a personalisation module, followed by the ’LAST MINUTE’ module. These modules serve quite different purposes and are further substructured in a different manner (for more details cf. (Rösner et al., 2012b) ). 2.1.1 Personalisation module Throughout the whole personalisation module the dominant mode of interaction is system initiative only, i.e. the system asks a question or gives a prompt. In other words this module is a series of dialog turns (or adjacency pairs (Jurafsky and Martin, 2008)) that are made up by a system question or prompt followed by the user’s answer or reaction. In some sense this module thus resembles more an investigative questioning than a symmetric dialog. 2.1.2 The LAST MINUTE module Selection In the bulk of ’LAST MINUTE’ the subject is expected to pack a suitcase for a two week holiday trip by choosing items from an online catalogue with twelve different categories 145 Proceedings of KONVENS 2012 (Main track: oral presentations), Vienna, September 20, 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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Page 97 and 98: Acknowledgments This work was funde
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Page 113 and 114: 5.2 Evaluation Metrics for WPD The
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Page 119 and 120: 2 Related Work The majority of pape
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Page 123 and 124: Features Dev Test CoNLL-2003 w 0 +
Page 125 and 126: Feature Dev Test Test+ — 93.78 91
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Page 137 and 138: Phonetically aided Syntactic Parsin
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Page 147 and 148: Table 1: Comparison between corpora
Page 149 and 150: Table 2: Most frequent POS patterns
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Page 153 and 154: 6 Summary We have presented the cur
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Page 157 and 158: s k n i i: f phh ohehnh ih xh s k n
Page 159 and 160: Dataset µ G µ P P [G>15] P [P >15
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Page 163 and 164: (1, 1) (2, 1) (3, 1) (4, 1) (1, 2)
Page 165 and 166: Use of linguistic features for impr
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Page 169 and 170: Accuracy of Accuracy of Accuracy of
Page 171 and 172: English Persian Train: Sentences 30
Page 173 and 174: to compensate for sparsity of paral
Page 175 and 176: Name # Documents # Tokens # Lemmas
Page 177 and 178: Figure 1: Precision-Recall graphs c
Page 179 and 180: A Multi-level Annotation Model for
Page 181 and 182: and sentence level with the aim of
Page 183 and 184: (Schmid, 1995; Schiller et al., 199
Page 185 and 186: EI EII Tag AKR MARK Typography ITER
Page 187 and 188: References Saima Aman and Stan Szpa
Page 189 and 190: Robust processing of noisy web-coll
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Page 193 and 194: 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
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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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