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

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[11] Jan Hajič, Alena Böhmová, Eva Hajičová, and Barbora Vidová-Hladká. The Prague Dependency Treebank: A Three-Level Annotation Scenario. In A. Abeillé, editor, Treebanks: Building and Using Parsed Corpora, pages 103–127. Amsterdam:Kluwer, 2000. [12] Angelina Ivanova, Stephan Oepen, Lilja Øvrelid, and Dan Flickinger. Who did what to whom? a contrastive study of syntacto-semantic dependencies. In Proceedings of the Sixth Linguistic Annotation Workshop, pages 2–11, Jeju, Republic of Korea, 2012. [13] Richard Johansson and Pierre Nugues. Extended Constituent-to-dependency Conversion for English. In Proceedings of NODALIDA 2007, Tartu, Estonia, 2007. [14] Tracy Holloway King, Richard Crouch, Stefan Riezler, Mary Dalrymple, and Ronald M. Kaplan. The parc 700 dependency bank. In In Proceedings of the 4th International Workshop on Linguistically Interpreted Corpora (LINC-03, pages 1–8, 2003. [15] Robert Malouf, John Carroll, and Ann Copestake. Efficient feature structure operations without compilation. Natural Language Engineering, 6:29–46, 2000. [16] Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. Building a large annotated corpus of english: The penn treebank. Computational Linguistics, 19(2):313–330, 1993. [17] Stephan Oepen. [incr tsdb()] — competence and performance laboratory. User manual. Technical report, Computational Linguistics, Saarland University, Saarbrücken, Germany, 2001. [18] Stephan Oepen, Kristina Toutanova, Stuart Shieber, Christopher Manning, Dan Flickinger, and Thorsten Brants. The LinGO Redwoods treebank: motivation and preliminary applications. In Proceedings of COLING 2002: The 17th International Conference on Computational Linguistics: Project Notes, Taipei, Taiwan, 2002. [19] Carl J. Pollard and Ivan A. Sag. Head-Driven Phrase Structure Grammar. University of Chicago Press, Chicago, USA, 1994. [20] Victoria Rosén, Paul Meurer, and Koenraad de Smedt. LFG Parsebanker: A Toolkit for Building and Searching a Treebank as a Parsed Corpus. In Frank Van Eynde, Anette Frank, Gertjan van Noord, and Koenraad De Smedt, editors, Proceedings of the 7th International Workshop on Treebanks and Linguistic Theories (TLT7), pages 127–133, Utrecht, 2009. LOT. [21] Mihai Surdeanu, Richard Johansson, Adam Meyers, Lluís Màrquez, and Joakim Nivre. The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies. In Proceedings of the 12th Conference on Computational Natural Language Learning (CoNLL-2008), Manchester, UK, 2008. [22] L. van der Beek, G. Bouma, R. Malouf, and G. van Noord. The alpino dependency treebank. In Computational Linguistics in the Netherlands (CLIN) 2001, Twente University, 2002. [23] Yi Zhang and Hans-Ulrich Krieger. Large-scale corpus-driven pcfg approximation of an hpsg. In Proceedings of the 12th International Conference on Parsing Technologies, pages 198–208, Dublin, Ireland, 2011. 96
ParDeepBank: Multiple Parallel Deep Treebanking Dan Flickinger 1 , Valia Kordoni 3,2 , Yi Zhang 2 , António Branco 4 , Kiril Simov 5 , Petya Osenova 5 , Catarina Carvalheiro 4 , Francisco Costa 4 , Sérgio Castro 4 1 Stanford University, USA, 2 DFKI GmbH, Germany, 3 Humboldt University of Berlin, Germany, 4 University of Lisbon, Portugal, 5 Bulgarian Academy of Sciences, Bulgaria danf@stanford.edu, evangelia.kordoni@anglistik.hu-berlin.de, yizhang@dfki.de, antonio.branco@di.fc.ul.pt, kivs@bultreebank.org, petya@bultreebank.org, catarina.carvalheiro@di.fc.ul.pt, f.costa@di.fc.ul.pt, sergio.castro@di.fc.ul.pt Abstract This paper describes the creation of an innovative and highly parallel treebank of three languages from different language groups — English, Portuguese and Bulgarian. The linguistic analyses for the three languages are done by compatible parallel automatic HPSG grammars using the same formalism, tools and implementation strategy. The final analysis for each sentence in each language consists of (1) a detailed feature structure analysis by the corresponding grammar and (2) derivative information such as derivation trees, constituent trees, dependency trees, and Minimal Recursion Semantics structures. The parallel sentences are extracted from the Penn Treebank and translated into the other languages. The Parallel Deep Bank (ParDeep- Bank) has potentially many applications: for HPSG grammar development; machine translation; evaluation of parsers on comparable data; etc. 1 Introduction In this paper we present the initial version of ParDeepBank — a parallel treebank for three languages: English, Portuguese, and Bulgarian. The annotation of each sentence in the treebank is automatically analyzed by a deep HPSG grammar (Head-driven Phrase Structure Grammar: [24]) for the corresponding language. These grammars are implemented within the same linguistic theory and formalism, following a similar approach to grammar implementation. The correct analysis is selected manually in all of the three cases. The HPSG grammars for the three languages have different levels of development. Hence, they differ in their coverage. In order to process the whole set of sentences in the parallel treebank, 97
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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
Page 39 and 40: Example
Page 41 and 42: ‏ Example 8: represent
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Page 45 and 46: Chinese) as the direct object NP.
Page 47 and 48: Example 15: Tokens and Word Forms
Page 49 and 50: In a second experiment, a dataset w
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Page 53 and 54: [3] Leech G. N., Barnett, R. & Kahr
Page 55 and 56: Effectively long-distance dependenc
Page 57 and 58: In French, another case of eLDD is
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Page 61 and 62: 4.2.3 Annotation methodology Becaus
Page 63 and 64: Number of occurrences in FTB +SEQTB
Page 65 and 66: 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
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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: will enable further improvements in
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 and 120: [9] G. Doddington, A. Mitchell, M.
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
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phrases are generated by a shallow
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Figure 2: A Kybot p
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SUJ NP SENT MOD PP NP PP PP NP NP P
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Checkpoint Instances Google PT Our
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6 Conclusions In this paper we have
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An integrated web-based treebank an
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16] and we have earlier reported fr
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Figure 2: Screenshot of the interfa
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ple subcategorization frames may be
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4 Integrated interface for annotati
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of the 5th International Conference
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Translational divergences and their
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posed so far, Cyrus’ work did not
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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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