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(14cz)poblahopřát ADDR(Dat)’congratulate +Dat’ (14ru)pozdravit’ ADDR(Acc) ’congratulate +Acc’ (15cz)děkovat ADDR(Dat) ’thank +Dat’ (15ru)blagodarit’ ADDR(Acc) ’to thank + Acc’ 4. Similar to the verbs of Exchange class, some Czech communication verbs with surface form (o+Acc)-(about+Acc) will be translated in another manner in Russian due to the fact that, unlike in Czech, the preposition ’o’-’about’ does not combine with the Accusative: (16cz)hlásit se PAT(o+Acc): hlásí se o slovo ’ask about word’ (16ru)prosit’ PAT(Gen) Ona prosit slova ’She ask word.gen’ (16en)’ask for a word’ Coincidental differences occurring only once or twice are not going to any scheme: (17cz)doznávat se PAT(k+Dat) ’confess to smth’ (17ru)priznavat’sja PAT(v+Loc) ’to confess in smth’ (18cz)konzultovat PAT(Acc) ’to consult +Acc’ (18ru)konsultirovat’ PAT(po+Loc)-(about+Loc) 4.5 Class of Mental Action Verbs of this class often have differences in valency frames, but they are rather coincidental and we have found only one regular difference - when Czech PAT(na+Acc)-(on+Acc) corresponds to Russian PAT (o+Loc)-(about+Loc), (pro+Acc)- (about+Acc) or (k+Dat)-(to+Dat). The surface form (na+Acc) in Czech is also different for verbs belonging in the class Communication, but for that class it was regularly translated as (o+Loc)-(about+Loc) whereas for the class of Mental Action no common translation equivalent exists. (19cz)pamatovat PAT(na+Acc) ’to remember on’ (19ru)pomnit’ PAT(pro+Acc) ’to remember about’ (20cz)myslet PAT(na+Acc) ’to think on’ (20ru)dumat’ PAT(o+Loc) ’to think about’ (21cz)zvykat si PAT(na+Acc) ’get used on’ (21ru)privykat’ PAT(k+Dat) ’to get used to’ The structure of the following verb coincides a lot with that from ex. (14) and (15) though the functor is PAT, not ADDR: (22cz)rozumět PAT(Dat) ’understand’ (22ru)ponimat’ PAT(Acc) ’understand’ Other coincidental differences: (23)pohrdat PAT(Ins) (23)prezirat’ PAT(Acc) ’to despice’ (24cz)mrzet ACT(Acc) (24ru)sožalet ACT(Nom) ’to be sorry for’ The example (24) is the one of a very few verbs with different surface realization of ACTor. 4.6 Overall results We have compared Czech and Russian valency frames of verbs from 5 semantic classes, totally 1473 lexical entries. 111(7.5%) of them were different in Czech and Russian. The comparison was rather straightforward because of the relatedness of the languages. If some more distant languages were compared, more complicated method of evaluation should be chosen. From the examples above we can make the following observations: • most dissimilarities occur in prepositional phrases. • the regular discrepancies are more frequent than the coincidental ones. • Within a verb class we can find some typical valency patterns of Czech verbs which correspond regularly to the different Russian pattern. The table 1 presents the distribution of verbs with different frames according to the verb classes. From this table we can see that verbs of physical activity(change, motion, exchange) have in 450 Proceedings of KONVENS 2012 (LexSem 2012 workshop), Vienna, September 21, 2012
Verb class same frame different frame # of verbs Change 309(95%) 14(5%) 323 Exchange 166(92%) 13(8%) 179 Motion 305(99%) 3(1%) 308 Communication 312(88%) 42(12%) 354 Mental Action 270(87%) 39(13%) 309 Total 1362(92%) 111(8%) 1473 Table 1: Differences according to the verb classes some sense less complicated valency structures than verbs of mental activity(communication, mental action) and that in most cases, their valency structure corresponds to that of Russian verbs. 5 Conclusion In this paper we have described the dissimilarities in Czech and Russian Valency based on the material of the Czech lexicon. Our main hypothesis was that the differences in valency structure might be explained by the semantics of verbs , so we have exploited the classification of the semantic classes provided by Vallex. In almost in each verb class we have found some regular dissimilarity that is typical of this class. Still, there are some cases when verbs from other classes are subjected to this regularity as well, so other aspects (such as surface realization) should also be taken into con- sideration. A practical result of our paper is that we have made a draft version of a small bilingual Czech-Russian lexicon with different frames in the Vallex format. Acknowledgments The research is supported by the grants P406/2010/0875 GAČR and GAUK 639012. References Ch. Fellbaum. 1998. WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press C. Baker, C. Fillmore and J. Lowe. 1998. The Berkeley FrameNet Project. Proceedings of the 17th international conference on Computational linguistics - Volume 1 (COLING ’98), Vol. 1. Association for Computational Linguistics, Stroudsburg, PA, USA, 86-90. V. Benešová and O. Bojar. 2006. Czech Verbs of Communication and the Extraction of their Frames. Proceedings of the 9th International Conference, TSD 2006, pages 29-36. J. Hana and A. Feldman. 2004. Portable Language Technology: Russian via Czech. Proceedings of the Midwest Computational Linguistics Colloquium, June 25-26, 2004, Bloomington, Indiana. D. Hlaváčková and A. Horák. 2006. VerbaLex - New Comprehensive Lexicon of Verb Valencies for Czech. Computer Treatment of Slavic and East European Languages. Bratislava, Slovakia: Slovenský národný korpus, p. 107-115. N. Klyueva and V. Kuboň. 2010. Verbal Valency in the MT Between Related Languages. Proceedings of Verb 2010, Interdisciplinary Workshop on Verbs, The Identification and Representation of Verb Features Universita di Pisa - Dipartimento di Linguistica, Pisa, Italy, pp. 160-164. M. Lopatková and J. Panevová. 2006. Recent developments in the theory of valency in the light of the Prague Dependency Treebank. In Mária Šimková, editor, Insight into Slovak and Czech Corpus Linguistic, pages 83-92. Veda Bratislava, Slovakia. M. Lopatková, Z. Žabokrtský and V. Benešová. 2006. Valency Lexicon of Czech Verbs VALLEX 2.0. Technical Report 34, UFAL MFF UK. I. Mel’čuk and A. Zholkovsky. 1984. Explanatory Combinatorial Dictionary of Modern Russian. Semantico-syntactic Studies of Russian Vocabulary. Vienna: Wiener Slawistischer Almanach. K. Oliva. 1989. A parser for Czech implemented in systems Q. Praha:MFF UK J. Panevová. 1994. Valency Frames and the Meaning of the Sentence. The Prague School of Structural and Functional Linguistics (ed. Ph. L. Luelsdorff), Amsterdam-Philadelphia, John Benjamins, pp. 223-243. P. Sgall, E. Hajičová and J. Panevová. 1986. The Meaning of the Sentence and Its Semantic and Pragmatic Aspects. Academia/Reidel Publishing Company, Prague,Czech Republic/Dordrecht, Netherlands 451 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
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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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Page 401 and 402: Digitised Historical Text: Does it
Page 403 and 404: 3 Related Work Previous research on
Page 405 and 406: 5.1 End-of-line Soft Hyphen Deletio
Page 407 and 408: quite difficult to get an idea of h
Page 409 and 410: References Ahmad Abdulkader and Mat
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Page 421 and 422: Figure 6: Architecture of Morphorm
Page 423 and 424: References Harald R. Baayen, R. Pie
Page 425 and 426: edition has been adopted. This is d
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Page 429 and 430: Word Lemma Class Subclass Type Tens
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Page 435 and 436: English) closely and discusses the
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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: whom/which something is taken. (7cz
Page 453 and 454: Construction Grammar are introduced
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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 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
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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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