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

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[20] M. Novák and Z. Žabokrtský. Resolving noun phrase coreference in Czech. In Proceedings of the 8th international conference on Anaphora Processing and Applications, DAARC’11, pages 24–34, Berlin, Heidelberg, 2011. Springer-Verlag. [21] M. Ogrodniczuk and M. Kopeć. End-to-end coreference resolution baseline system for Polish, 2011. [22] M. Poesio. The mate/gnome proposals for anaphoric annotation, revisited. In In Michael Strube and Candy Sidner (editors), Proceedings of the 5th SIGdial Workshop on Discourse and Dialogue, pages 154–162, 2004. [23] K. Raghunathan, H. Lee, S. Rangarajan, N. Chambers, M. Surdeanu, D. Jurafsky, and C. D. Manning. A multi-pass sieve for coreference resolution. In EMNLP, pages 492–501, 2010. [24] M. Recasens, L. Màrquez, E. Sapena, M. A. Martí, M. Taulé, V. Hoste, M. Poesio, and Y. Versley. Semeval-2010 task 1: Coreference resolution in multiple languages. In Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval’10, pages 1–8, Stroudsburg, PA, USA, 2010. [25] W. M. Soon, D. Chung, Y. Lim, and H. T. Ng. A machine learning approach to coreference resolution of noun phrases, 2001. [26] O. Uryupina. Corry: A system for coreference resolution. In Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval’10, pages 100–103, Stroudsburg, PA, USA, 2010. [27] Y. Versley, S. P. Ponzetto, M. Poesio, V. Eidelman, A. Jern, J. Smith, X. Yang, and A. Moschitti. Bart: a modular toolkit for coreference resolution. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session, HLT- Demonstrations ’08, pages 9–12, Stroudsburg, PA, USA, 2008. Association for Computational Linguistics. [28] R. Vieira and M. Poesio. An empirically based system for processing definite descriptions. Comput. Linguist., 26(4):539–593, December 2000. 126
Analyzing the Most Common Errors in the Discourse Annotation of the Prague Dependency Treebank Pavlína Jínová, Jiří Mírovský, Lucie Poláková Charles University in Prague Institute of Formal and Applied Linguistics Malostranské nám. 25, Prague 1, Czech Republic E-mail: (jinova|mirovsky|polakova)@ufal.mff.cuni.cz Abstract We present an analysis of the inter-annotator discrepancies of the Czech discourse annotation in the Prague Dependency Treebank 2.0. Having finished the annotation of the inter-sentential semantic discourse relations with explicit connectives in the treebank, we report now on the results of the evaluation of the parallel (double) annotations, which is an important step in the process of checking the quality of the data. After we shortly describe the annotation and the method of the inter-annotator agreement measurement, we present the results of the measurement and, most importantly, we classify and analyze the most common types of annotators’ disagreement. 1 Discourse in the Prague Dependency Treebank 2.0 The Prague Dependency Treebank 2.0 (PDT; Hajič et al. [3]) is a manually annotated corpus of Czech journalistic texts, annotated on three layers of language description: morphological, analytical (the surface syntactic structure), and tectogrammatical (the deep syntactic structure) (Hajič et al. [2]). On the tectogrammatical layer, the data consist of almost 50,000 sentences. Annotation of discourse structure in PDT uses a lexical approach, similarly to one of the Penn Discourse Treebank (PDTB 2.0, Prasad et al. [9]). It is based on identifying a discourse connective (an expression with text structuring function), which takes two text segments as its arguments and indicates a discourse meaning between them. The annotators mark three basic types of information: the connective (contrary to the Penn approach, there is no list of possible discourse connectives in advance), the two arguments of the connective (mostly clauses, sentences but sometimes also larger units, such as paragraphs) and the semantic type of the relation. At this stage of the project, we do not annotate implicit relations (relations without a connective). 127
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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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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 and 118: Nominal P R F1 MUC 75.33% 81.33% 78
Page 119: [9] G. Doddington, A. Mitchell, M.
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
Page 169 and 170: 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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