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

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[3] Fort, K. and Sagot, B. (2010). Influence of Pre-annotation on POS-tagged Corpus Development. In Xue, N. and Poesio, M., editors, Proceedings of the fourth linguistic annotation workshop, pages 56–63, Stroudsburg. Association for Computational Linguistics. [4] Kromann, M. T. (2003). The Danish Dependency Treebank and the DTAG treebank tool. In Proceedings from the Second Workshop on Treebanks and Linguistic Theories (TLT 2003). [5] McDonald, R., Crammer, K., and Pereira, F. (2005). Online large-margin training of dependency parsers. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 91–98, Morristown, NJ, USA. Association for Computational Linguistics. [6] Nivre, J., Nilsson, J., and Hall, J. (2006). Talbanken05: A Swedish treebank with phrase structure and dependency annotation. In Proceedings of LREC, pages 1392–1395. [7] Nivre, J., Nilsson, J., Hall, J., Chanev, A., Eryiit, G., Kübler, S., Marinov, S., and Marsi, E. (2007). MaltParser: A language-independent system for datadriven dependency parsing. Natural Language Engineering, 13(2):95–135. [8] Petrov, S., Das, D., and McDonald, R. (2011). A Universal Part-of-Speech Tagset. [9] Søgaard, A. (2011). Data point selection for cross-language adaptation of dependency parsers. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 682–686. [10] Täckström, O., McDonald, R., and Uszkoreit, J. (2012). Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 477–487. Association for Computational Linguistics. [11] Zeman, D. (2008). Reusable Tagset Conversion Using Tagset Drivers. In Proceedings of the Language Resources and Evaluation Conference 2008. [12] Zeman, D. and Resnik, P. (2008). Cross-Language Parser Adaptation between Related Languages. In Singh, A. K., editor, Proceedings of the IJCNLP-08 Workshop on NLP for Less Privileged Languages, pages 35–42, Hyderabad, India. Asian Federation of Natural Language Processing. 198
Genitives in Hindi Treebank: An Attempt for Automatic Annotation Nitesh Surtani, Soma Paul Language Technologies Research Centre IIIT Hyderabad Hyderabad, Andhra Pradesh-500032 nitesh.surtaniug08@students.iiit.ac.in, soma@iiit.ac.in Abstract Building syntactic Treebank manually is a time consuming and human labor intensive task. The correctness of annotated data is very important because this resource can be used for developing important NLP tools such as syntactic parsers. In this paper, we examine genitive construction in Hindi Treebank with a view of developing a set of rules for automatic annotation of genitive data in Hindi Treebank. The rules perform quite well producing an overall 89% accuracy for right attachment of genitive noun with its head and correct labeling for the attachment. 1 Introduction A syntactically annotated Treebank is a highly useful language resource for many NLP tasks including parsing, grammar induction to name a few. Generally, building a Treebank requires an enormous effort by the annotators. But some constructions in Treebank can be automatically annotated. This on one hand reduces the human effort by decreasing the number of intervention required by the annotator, and on other hand helps to maintain consistent annotation. For the automatic annotation of the data, 3 types of cases exist: (1) constructions that have a unique cue that identifies it accurately; (2) construction which occur in varied contexts but still can be identified accurately with well-designed rules; and (3) constructions that cannot be handled using cues. Case 2 constructions are the interesting ones which require special attention for their automatic annotation. Genitive construction in Hindi is one such interesting construction that occurs in varied contexts. Though, noun with genitive case marker generally modifies a noun, it is also found to occur in other contexts including in relation with verbs, with complex predicates etc. In this paper, we will examine the distribution of genitive data in Hindi dependency Treebank. The aim is to study syntactic cues from the Treebank for determining the legitimate head of the genitive modifier and also identify the relation between the two. We implement the cues as rules for predicting the correct attachment between genitive noun and its head. This is an attempt towards developing semi-automatic annotation of Treebank for the genitive data. The paper is divided as follows: A detailed study of genitive data in Hindi has been carried out in section 2. Section 3 presents a brief overview of Hindi Treebank and Section 4 talks about distribution of genitives in Hindi Treebank. Section 5 then discusses a rule based approach for automatic annotation of genitive data. The Results and Observation are presented in Section 6 and Section 7 concludes the paper. 199
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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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gerundives for a total amount of so
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object+indirect object/object one.
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(VP (VB patch) ) ) ) (VP (VBZ is) (
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Types Adverb. Adject. Verbs Nouns T
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Eventually we may comment that ther
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DeepBank: A Dynamically Annotated T
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from another already existing one,
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to parse now does. The extra manual
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In the derivation show in Figure 1,
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4 Patching Coverage Gaps with An Ap
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will enable further improvements in
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ParDeepBank: Multiple Parallel Deep
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2 The ParDeepBank The PTB has emerg
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undergone extensive scientific scru
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the second combines the structures
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Sofia University). Each sentence wa
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data, and improvements in the infra
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The Effect of Treebank Annotation G
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ut without feature structures. This
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only the lexicon is fine-grained to
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Automatic Coreference Annotation in
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manually annotated Czech sentences.
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citizens of Bilbao] are very involv
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4.1.3 Coreference Selector Module T
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Nominal P R F1 MUC 75.33% 81.33% 78
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[9] G. Doddington, A. Mitchell, M.
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Analyzing the Most Common Errors in
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Graph 1 shows results of subsequent
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in the semantic type). This situati
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Will a Parser Overtake Achilles? Fi
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annotation is also based on a depen
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Set Name Sentences Tokens % Train/T
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dency relations (PRED, OBJ, SBJ, AD
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ton and Stacklazy), we trained a mo
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Feature Function Column Name Addres
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Using Parallel Treebanks for Machin
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Page 149 and 150: 6 Conclusions In this paper we have
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Page 183 and 184: Det Adj N (a) Danish Det Adj N (b)
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Page 191: mance is not inconsiderable, as was
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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