A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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Acknowledgments This work was partly funded by the French ANR project EDyLex (ANR-09-CORD-008). References [1] Abeillé, A., Clément, L., and Toussenel, F. (2003). Building a Treebank for French. Kluwer, Dordrecht. [2] Bies, A., Mott, J., Warner, C., and Kulick, S. (2012). English web treebank. Technical report, Linguistic Data Consortium„ Philadelphia, PA, USA. [3] Candito, M. and Crabbé, B. (2009). Improving generative statistical parsing with semi-supervised word clustering. In Proc. of IWPT’09, Paris, France. [4] Candito, M., Nivre, J., Denis, P., and Henestroza, E. (2010). Benchmarking of statistical dependency parsers for french. In Proc. of CoLing’10, Beijing, China. [5] Chrupała, G., Dinu, G., and van Genabith, J. (2008). Learning morphology with morfette. In In Proceedings of LREC 2008, Marrakech, Morocco. [6] Crabbé, B. and Candito, M. (2008). Expériences d’analyse syntaxique statistique du français. In Proc. of TALN’08, pages 45–54, Senlis, France. [7] Denis, P. and Sagot, B. (2009). Coupling an annotated corpus and a morphosyntactic lexicon for state-of-the-art POS tagging with less human effort. In Proc. of PACLIC, Hong Kong, China. [8] Foster, J. (2010). “cba to check the spelling”: Investigating parser performance on discussion forum posts. In Proc. of HLT/NAACL’10, Los Angeles, USA. [9] Foster, J., Cetinoglu, O., Wagner, J., Le Roux, J., Hogan, S., Nivre, J., Hogan, D., and van Genabith, J. (2011a). #hardtoparse: Pos tagging and parsing the twitterverse. In Proc. of the AAAI 2011 Workshop On Analyzing Microtext. [10] Foster, J., Cetinoglu, O., Wagner, J., Le Roux, J., Nivre, J., Hogan, D., and van Genabith, J. (2011b). From news to comment: Resources and benchmarks for parsing the language of web 2.0. In proc of IJCNLP, Chiang Mai, Thailand. [11] Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., and Smith, N. A. (2011). Part-ofspeech tagging for twitter: Annotation, features, and experiments. In Proc. of ACL’11, Portland, USA. [12] Petrov, S., Barrett, L., Thibaux, R., and Klein, D. (2006). Learning accurate, compact, and interpretable tree annotation. In Proc. of ACL’06, Sydney, Australia. [13] Seddah, D., Sagot, B., Candito, M., Mouilleron, V., and Combet, V. (2012). The french social media bank: a treebank of noisy user generated content. In Proceedings of CoLing’12, Mumbai, India. 186
Impact of treebank characteristics on cross-lingual parser adaptation Arne Skjærholt, Lilja Øvrelid Department of informatics, University of Oslo {arnskj,liljao}@ifi.uio.no Abstract Treebank creation can benefit from the use of a parser. Recent work on cross-lingual parser adaptation has presented results that make this a viable option as an early-stage preprocessor in cases where there are no (or not enough) resources available to train a statistical parser for a language. In this paper we examine cross-lingual parser adaptation between three highly related languages: Swedish, Danish and Norwegian. Our focus on related languages allows for an in-depth study of factors influencing the performance of the adapted parsers and we examine the influence of annotation strategy and treebank size. Our results show that a simple conversion process can give very good results, and with a few simple, linguistically informed, changes to the source data, even better results can be obtained, even with a source treebank that is quite differently annotated from the target treebank. We also show that for languages with large amounts of lexical overlap, delexicalisation is not necessary, indeed lexicalised parsers outperform delexicalised parsers, and that for some treebanks it is possible to convert dependency relations and create a labelled cross-lingual parser. 1 1 Introduction It is well known that when annotating new resources, the best way to go about it is not necessarily to annotate raw data, but rather to correct automatically annotated material (Chiou et al. [2], Fort and Sagot [3]). This can yield important speed gains, and often also improvements in measures of annotation quality such as interannotator agreement. However, if we wish to create a treebank for a language with no pre-existing resources, we face something of a Catch-22; on the one hand, we have no data to train a statistical parser to automatically annotate sentences, but on the other hand we really would like to have one. In this setting, one option is cross-lingual parser adaptation. 1 The code used to obtain the data in this paper are available from https://github.com/ arnsholt/tlt11-experiments. 187
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A Treebank-based Investigation of I
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participle and a (te-)infinitival c
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Some verbs occur twice, since they
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Profiling Feature Selection for Nam
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prepositional objects (FOPP, OPP).
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the limited size of annotated data
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with high precision typically have
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‘This was “not significantly”
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The preposition durch typically has
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Non-Projective Structures in Indian
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the sequential order of nodes in a
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extra-posed relative clause that ge
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Experiments on Dependency Parsing o
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for mitigating the effects of spars
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obtained with MALTParser is 76.61%
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as a standardised serialisation for
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constituency and dependency, possib
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SynAF and/or in . However, they sha
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shows how some elements that are no
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Example
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‏ Example 8: represent
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Chinese) as the direct object NP.
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Example 15: Tokens and Word Forms
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In a second experiment, a dataset w
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[3] Leech G. N., Barnett, R. & Kahr
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Effectively long-distance dependenc
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In French, another case of eLDD is
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elativization, it-clefts or questio
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4.2.3 Annotation methodology Becaus
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Number of occurrences in FTB +SEQTB
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producing treebank based LFG approx
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Logical Form Representation for Lin
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gerundives for a total amount of so
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object+indirect object/object one.
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(VP (VB patch) ) ) ) (VP (VBZ is) (
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Types Adverb. Adject. Verbs Nouns T
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Eventually we may comment that ther
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DeepBank: A Dynamically Annotated T
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from another already existing one,
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to parse now does. The extra manual
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In the derivation show in Figure 1,
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4 Patching Coverage Gaps with An Ap
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will enable further improvements in
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ParDeepBank: Multiple Parallel Deep
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2 The ParDeepBank The PTB has emerg
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undergone extensive scientific scru
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the second combines the structures
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Sofia University). Each sentence wa
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data, and improvements in the infra
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The Effect of Treebank Annotation G
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ut without feature structures. This
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only the lexicon is fine-grained to
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Automatic Coreference Annotation in
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manually annotated Czech sentences.
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citizens of Bilbao] are very involv
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4.1.3 Coreference Selector Module T
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Nominal P R F1 MUC 75.33% 81.33% 78
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[9] G. Doddington, A. Mitchell, M.
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Analyzing the Most Common Errors in
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Graph 1 shows results of subsequent
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in the semantic type). This situati
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Will a Parser Overtake Achilles? Fi
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Page 149 and 150: 6 Conclusions In this paper we have
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Page 163 and 164: Translational divergences and their
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Page 177 and 178: Phenomenon Attested example Std. co
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Page 183 and 184: Det Adj N (a) Danish Det Adj N (b)
Page 185 and 186: sv: Bestämmelserna i detta avtal f
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Page 191 and 192: mance is not inconsiderable, as was
Page 193 and 194: Genitives in Hindi Treebank: An Att
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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