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

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2% lower than the HPSG parser with ERG. The PCFG succeeds in parsing over 99% of the test set, while the original ERG successfully covers about 80% of it. In a comparison to the parsing accuracy of the state-of-the-art Berkeley parser trained with the same corpus, our PCFG training was much more scalable (with up to 50 million automatically ERG parsed trees), yielding much better overall accuracy. Lastly, we have developed a graphical tree editor that allows the annotators to manually correct the remaining errors in the PCFG parses. The tool not only supports an intuitive drag-and-drop style of editing, but also records the entire editing sequence, creating additional raw annotation data for future research. Preliminary experience on the post-editing steps suggests that an annotator can correct 35-40 sentences per hour, producing for each a derivation tree which contains at least one constituent not (yet) licensed by the ERG, but necessary for the correct analysis of the sentence. 5 Next Steps Among the principal advantages claimed for this DeepBank approach is the ability to make successive refinements to the treebank annotations, by making changes to the grammar or to the parsing configuration, and then reparsing and updating with the existing discriminant-based annotations. One planned change in that parsing configuration is to record in the database the full (packed) parse forest for each sentence, rather than the 500 highest-ranked parses currently stored. Manual disambiguation from the full forest will require a new treebanking tool, still under development, but initial experiments already confirm that the existing discriminants are sufficient to automatically fully disambiguate the great majority of the previously treebanked WSJ sentences even working with full parse forests. This full-forest method will provide greater stability in the English DeepBank, eliminating the current minor but annoying uncertainty that results from the dependence on parse ranking to preserve the desired analysis among the top-ranked 500. 6 Conclusion The English DeepBank provides linguistically rich syntactic and semantic annotations grounded in a well-established and leading linguistic theory (HPSG) for a large and familiar corpus, the million-word Wall Street Journal portion also annotated in the Penn Treebank. The first public release of this resource will include manually selected full analyses produced by the English Resource Grammar for more than 80% of these 50,000 sentences, providing unmatched consistency and linguistic detail, available in multiple formats and representations. The remainder of the corpus will be annotated with compatible though approximate syntactic and semantic analyses produced using a PCFG trained on the manually annotated treebank, to ensure complete coverage of the corpus in the treebank. Adopting the Redwoods methodology for constructing and maintaining this dynamic treebank 94
will enable further improvements in the grammar to be projected into updated versions of the DeepBank, along with correction of any remaining annotation errors. We believe that an annotated resource of this scale for this corpus will be useful for research both in NLP and in corpus-based theoretical work in linguistics and psycholinguistics. Acknowledgements The project is partially supported by the Erasmus Mundus European Masters Program in Language and Communication Technologies (http://www.lct-master.org; EM Grant Number: 2007-0060). The second author also thanks the Deependance project funded by BMBF (01IW11003) for its support of the work. References [1] Peter Adolphs, Stephan Oepen, Ulrich Callmeier, Berthold Crysmann, Dan Flickinger, and Bernd Kiefer. Some fine points of hybrid natural language parsing. In Proceedings of the 6th International Conference on Language Resources and Evaluation, Marrakech, Morocco, 2008. [2] Sabine Brants, Stefanie Dipper, Silvia Hansen, Wolfgang Lezius, and George Smith. The tiger treebank. In Proceedings of the workshop on treebanks and linguistic theories, pages 24–41, 2002. [3] Thorsten Brants. TnT - a statistical part-of-speech tagger. In Proceedings of the 6th Conference on Applied Natural Language Processing (ANLP 2000), Seattle, USA, 2000. [4] Ulrich Callmeier. Efficient parsing with large-scale unification grammars. Master’s thesis, Universität des Saarlandes, Saarbrücken, Germany, 2001. [5] Jean Carletta. Assessing agreement on classification tasks: the kappa statistic. Computational Linguistics, 22(2):249–254, 1996. [6] David Carter. The treebanker: a tool for supervised training of parsed corpora. In Proceedings of the ACL Workshop on Computational Environments for Grammar Development and Linguistic Engineering, pages 9–15, Madrid, Spain, 1997. [7] Ann Copestake. Implementing Typed Feature Structure Grammars. CSLI, Stanford, USA, 2002. [8] Ann Copestake, Dan Flickinger, Carl J. Pollard, and Ivan A. Sag. Minimal recursion semantics: an introduction. Research on Language and Computation, 3(4):281–332, 2005. [9] Stefanie Dipper. Grammar-based corpus annotation. In Anne AbeillÃ c○, Thorsten Brants, and Hans Uszkoreit, editors, Proceedings of the Workshop on Linguistically Interpreted Corpora LINC-2000 , Luxembourg, pages 56–64, 2000. [10] Dan Flickinger. On building a more efficient grammar by exploiting types. In Stephan Oepen, Dan Flickinger, Jun’ichi Tsujii, and Hans Uszkoreit, editors, Collaborative Language Engineering, pages 1–17. CSLI Publications, 2002. 95
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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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Page 39 and 40: Example
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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
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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: 4 Patching Coverage Gaps with An Ap
Page 91 and 92: ParDeepBank: Multiple Parallel Deep
Page 93 and 94: 2 The ParDeepBank The PTB has emerg
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Page 103 and 104: The Effect of Treebank Annotation G
Page 105 and 106: ut without feature structures. This
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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
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Page 137 and 138: Feature Function Column Name Addres
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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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