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

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the anaphor and its five most probable antecedents. Our results demonstrate that, despite their simplicity, ML and deterministic models for coreference resolution obtain competitive results. This will allow us to create an automatic annotation system to improve the manual annotation process of the corpora. A larger tagged corpus, in turn, will enable us to improve the performance of our automatic system. Acknowledgements This work has been supported by Hibrido Sint (TIN2010-20218) project. References [1] I. Aduriz, K. Ceberio, and A. Díaz de Ilarraza. Pronominal anaphora in Basque: Annotation issues for later computational treatment. In DAARC2007 Lagos (Portugal); pp. 1-7, ISBN: 978-989-95343-0-8, 2007. [2] I. Alegria, X. Artola, K. Sarasola, and M. Urkia. Automatic morphological analysis of Basque. 1996. [3] I. Alegria, N. Ezeiza, and I. Fernandez. Named entities translation based on comparable corpora. In Multi-Word-Expressions in a Multilingual Context Workshop on EACL06. April 3. pp.1-8. Trento, 2006. [4] O. Arregi, K. Ceberio, A. Díaz de Ilarraza, I. Goenaga, B. Sierra, and A Zelaia. A first machine learning approach to pronominal anaphora resolution in Basque. In IBERAMIA 2010. LNAI 6433, 2010. [5] E. Bengtson and D. Roth. Understanding the value of features for coreference resolution. In In EMNLP, 2008. [6] A. Björkelund and P. Nugues. Exploring lexicalized features for coreference resolution. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, CONLL Shared Task’11, pages 45–50, Stroudsburg, PA, USA, 2011. [7] S. Broscheit, M. Poesio, S. Paolo Ponzetto, K. J. Rodriguez, L. Romano, O. Uryupina, Y. Versley, and R. Zanoli. BART: A multilingual anaphora resolution system. In Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval’10, pages 104–107, Stroudsburg, PA, USA, 2010. [8] K. Chang, R. Samdani, A. Rozovskaya, N. Rizzolo, M. Sammons, and D. Roth. Inference protocols for coreference resolution. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, CONLL Shared Task ’11, pages 40–44, Stroudsburg, PA, USA, 2011. 124
[9] G. Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel, and R. Weischedel. The Automatic Content Extraction (ACE) Program–Tasks, Data, and Evaluation. Proceedings of LREC 2004, pages 837–840, 2004. [10] C. N. dos Santos and D. L. Carvalho. Rule and tree ensembles for unrestricted coreference resolution. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, CONLL Shared Task’11, pages 51–55, Stroudsburg, PA, USA, 2011. [11] J. M. Arriola A. Atutxa A. Diaz-De-Ilarraza N. Ezeiza K. Gojenola M. Oronoz A. Soroa R. Urizar I. Aduriz, M. J. Aranzabe. Methodology and steps towards the construction of EPEC, a corpus of written Basque tagged at morphological and syntactic levels for the automatic processing. pages 1–15. Rodopi. Book series: Language and Computers, 2006. [12] H. Kobdani and H. Schütze. Sucre: A modular system for coreference resolution. In Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval’10, pages 92–95, Stroudsburg, PA, USA, 2010. [13] H. Lee, Y. Peirsman, A. Chang, N. Chambers, M. Surdeanu, and D. Jurafsky. Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL- 2011 Shared Task. In Proceedings of the CoNLL-2011 Shared Task, 2011. [14] N. G. Linh. Rule-based approach to pronominal anaphora resolution applied on the Prague dependency treebank 2.0 data, 2007. [15] A. Díaz de Ilarraza L. Moreno E. Bisbal-M. J. Aranzabe A. Ageno M. A. Marti F. Navarro M. S. Palomar, M. Civit. Construcción de una base de datos de arboles sintáctico-semánticos para el catalán, euskera y español. In XX. Congreso SEPLN, 2004. [16] N. S. Moosavi and G. Ghassem-Sani. A ranking approach to Persian pronoun resolution. Advances in Computational Linguistics. In Advances in Computational Linguistics. Research in Computing Science 41, pages 169–180, 2009. [17] MUC-6. Coreference task definition (v2.3, 8 sep 95). In Proceedings of the Sixth Message Understanding Conference (MUC-6), pages 335–344, Columbia, Maryland, USA, November 6–8 1995. [18] MUC-7. Coreference task definition (v3.0, 13 jul 97). In Proceedings of the 7th Message Understanding Conference (MUC-7), Fairfax, Virginia, USA, April 29–May 1 1998. [19] C. Müller and M. Strube. Multi-level annotation of linguistic data with MMAX2. In Sabine Braun, Kurt Kohn, and Joybrato Mukherjee, editors, Corpus Technology and Language Pedagogy: New Resources, New Tools, New Methods, pages 197–214. Peter Lang, Frankfurt a.M., Germany, 2006. 125
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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
Page 67 and 68: Logical Form Representation for Lin
Page 69 and 70: gerundives for a total amount of so
Page 71 and 72: object+indirect object/object one.
Page 73 and 74: (VP (VB patch) ) ) ) (VP (VBZ is) (
Page 75 and 76: Types Adverb. Adject. Verbs Nouns T
Page 77 and 78: Eventually we may comment that ther
Page 79 and 80: DeepBank: A Dynamically Annotated T
Page 81 and 82: from another already existing one,
Page 83 and 84: to parse now does. The extra manual
Page 85 and 86: In the derivation show in Figure 1,
Page 87 and 88: 4 Patching Coverage Gaps with An Ap
Page 89 and 90: will enable further improvements in
Page 91 and 92: ParDeepBank: Multiple Parallel Deep
Page 93 and 94: 2 The ParDeepBank The PTB has emerg
Page 95 and 96: undergone extensive scientific scru
Page 97 and 98: the second combines the structures
Page 99 and 100: Sofia University). Each sentence wa
Page 101 and 102: data, and improvements in the infra
Page 103 and 104: The Effect of Treebank Annotation G
Page 105 and 106: ut without feature structures. This
Page 107 and 108: only the lexicon is fine-grained to
Page 109 and 110: Automatic Coreference Annotation in
Page 111 and 112: manually annotated Czech sentences.
Page 113 and 114: citizens of Bilbao] are very involv
Page 115 and 116: 4.1.3 Coreference Selector Module T
Page 117: Nominal P R F1 MUC 75.33% 81.33% 78
Page 121 and 122: Analyzing the Most Common Errors in
Page 123 and 124: Graph 1 shows results of subsequent
Page 125 and 126: in the semantic type). This situati
Page 127 and 128: Will a Parser Overtake Achilles? Fi
Page 129 and 130: annotation is also based on a depen
Page 131 and 132: Set Name Sentences Tokens % Train/T
Page 133 and 134: dency relations (PRED, OBJ, SBJ, AD
Page 135 and 136: ton and Stacklazy), we trained a mo
Page 137 and 138: Feature Function Column Name Addres
Page 139 and 140: Using Parallel Treebanks for Machin
Page 141 and 142: phrases are generated by a shallow
Page 143 and 144: Figure 2: A Kybot p
Page 145 and 146: SUJ NP SENT MOD PP NP PP PP NP NP P
Page 147 and 148: Checkpoint Instances Google PT Our
Page 149 and 150: 6 Conclusions In this paper we have
Page 151 and 152: An integrated web-based treebank an
Page 153 and 154: 16] and we have earlier reported fr
Page 155 and 156: Figure 2: Screenshot of the interfa
Page 157 and 158: ple subcategorization frames may be
Page 159 and 160: 4 Integrated interface for annotati
Page 161 and 162: of the 5th International Conference
Page 163 and 164: Translational divergences and their
Page 165 and 166: posed so far, Cyrus’ work did not
Page 167 and 168: allowed an appropriate coverage of
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damentales [...] 10 (the universal
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for all lex_pair(s,t) do if head an
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the treatment of typical translatio
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Building a treebank of noisy user-g
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Phenomenon Attested example Std. co
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Figure 1: French Social Media Bank
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Impact of treebank characteristics
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Det Adj N (a) Danish Det Adj N (b)
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sv: Bestämmelserna i detta avtal f
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100 90 80 Unlabelled attachment 70
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vidner , tilhørere og tiltalte wit
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mance is not inconsiderable, as was
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Genitives in Hindi Treebank: An Att
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A genitive can occur with complex p
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quite high. This motivates us towar
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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