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

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parallel Bulgarian/English corpora which represent the gold standard corpus for word level alignment between Bulgarian and English. For the rest of the data we are following the approach, undertaken for the alignment of the Portuguese DeepBank to English DeepBank. The word level alignment between Portuguese and English is done in two steps. First, GIZA++ tool 4 is trained on the parallel corpus resulting from the translation of WSJ into Portuguese. This training produces an alignment model which is tuned with respect to this particular project. After this automatic step, a manual checking with a correction procedure is performed. The manual step was performed by the alignment editing tool in the Sanchay collection of NLP tools 5 . The alignment on the syntactic and semantic levels is dynamically constructed from the sentence and word level alignment as well as the current analyses of the sentences in ParDeepBank. The syntactic and semantic analyses in all three treebanks are lexicalized, thus the word level alignment is a good starting point for establishing of alignment also on the upper two levels. As one can see in the guidelines for Bulgarian/English word level alignment, the non-compositional phrases (i.e. idioms and collocations) are aligned on word level. Thus, their special syntax and semantics is captured already on this first level of alignment. Then, considering larger phrases, we establish syntactic and semantic alignment between the corresponding analyses only if their lexical realizations in the sentence are aligned on word level. This mechanism for alignment on different levels has at least two advantages: (1) it allows the exploitation of word level alignment which is easier for the annotators; and (2) it provides a flexible way for updating of the existing syntactic and semantic alignments when the DeepBank for one or more languages is updated after an improvement has been made in the corresponding grammar. In this way, we have adopted the idea of dynamic treebanking to the parallel treebanking. It provides an easy way for improving the quality of the linguistic knowledge encoded in the parallel treebanks. Also, this mechanism of alignment facilitates the additions of DeepBanks for other languages or additions of analyses in other formalisms. 6 Conclusion and Future Work In this paper we presented the design and initial implementation of a scalable approach to parallel deep treebanking in a constraint-based formalism, beginning with three languages for which well-developed grammars already exist. The methods for translation and alignment at several levels of linguistic representation are viable, and our experience confirms that the monolingual deep treebanking methodology can be extended quite successfully in a parallel multi-lingual context, when using the same data and the same grammar architecture. The next steps of the ParDeepBank development are the expansion of the volume of aligned annotated 4 http://code.google.com/p/giza-pp/ 5 http://sanchay.co.in/ 106
data, and improvements in the infrastructure for supporting the creation, maintenance and exploitation of these dynamic Parallel DeepBanks. References [1] Bender, E. M., Flickinger, D., and Oepen, S. 2002. The Grammar Matrix. An Open- Source Starter-Kit for the Rapid Development of Cross-Linguistically Consistent Broad-Coverage Precision Grammar. In Proc. of the Workshop on Grammar Engineering and Evaluation at COLING’02. [2] Bond, F., Oepen, S., Siegel, M., Copestake, A., and Flickinger, D. 2005. Open- Source Machine Translation with DELPH-IN. In Proc. of the Open-Source Machine Translation Workshop at the 10th MTS, pp. 15–22. [3] Branco, A., and Silva, J. 2006a. Dedicated Nominal Featurization of Portuguese. In Proc. of the 7th International Conference on Computational Processing of the Portuguese Language, PROPOR’06, pp. 244–247. [4] Branco, A., and Silva, J. 2006b. A Suite of Shallow Processing Tools for Portuguese: LX-Suite. In Proc. of the Eleventh Conference of the EACL: Posters & Demonstrations, EACL’06, pp. 179–182. [5] Branco, A., and Costa, F. 2007a. Accommodating Language Variation in Deep Processing. In Proc. of GEAF07 Workshop, pp. 67–86. [6] Branco, A., and Costa, F. 2007b Self- or Pre-Tuning? Deep Linguistic Processing of Language Variants. In Proc. of the ACL Workshop on Deep Linguistic Processing. [7] Branco, A., and Costa, F. 2008. LXGram in the Shared Task "Comparing Semantic Representations" of STEP2008. In Proc. of the 2008 Conference on Semantics in Text Processing, pp. 299–310. [8] Branco, A., and Costa, F. 2010. LXGram: A Deep Linguistic Processing Grammar for Portuguese. In LNAI, 6001, pp. 86–89. [9] Branco, A., Costa, F., Silva, J., Silveira, S., Castro, S., Avelãs, M., Pinto, C., and Graça, J. 2010. Developing a Deep Linguistic Databank Supporting a Collection of Treebanks: CINTIL DeepGramBank. In Proc. of LREC’10. [10] Branco, A., and Nunes, F. 2012. Verb Analysis in a Highly Inflective Language with an MFF Algorithm. In Proc. of the 10th international conference on Computational Processing of the Portuguese Language, PROPOR’12, pp. 1–11. [11] Copestake, A. 2002. Implementing Typed Feature Structure Grammars. CSLI Publications. [12] Copestake, A., and Flickinger, D. 2000. An Open-Source Grammar Development Environment and Broad-Coverage English Grammar Using HPSG. In Proc. of the LREC00. [13] Copestake, A., Flickinger, D., Pollard, C., and Sag, I. 2005. Minimal Recursion Semantics: an Introduction. Research on Language and Computation, 3(4). [14] Crysmann, B. 2007. Local Ambiguity Packing and Discontinuity in German. In Proc. of the ACL Workshop on Deep Linguistic Processing. 107
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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
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
Page 59 and 60: elativization, it-clefts or questio
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
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: Sofia University). Each sentence wa
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
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
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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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