Abstract
This paper introduces a work on identification of conjunct verbs in Hindi. The paper will first focus on investigating which noun-verb combination makes a conjunct verb in Hindi using a set of linguistic diagnostics. We will then see which of these diagnostics can be used as features in a MaxEnt based automatic identification tool. Finally we will use this tool to incorporate certain features in a graph based dependency parser and show an improvement over previous best Hindi parsing accuracy.
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References
Hook, P.E.: The Hindi compound verb: What it is and what it does? In: Singh, K.S. (ed.) Readings in Hindi-Urdu Linguistics. National Publishing House, Delhi (1974)
Mohanan, T.: Wordhood and Lexicality. NLLT 13, 75–134 (1995)
Alsina, A.: Complex Predicates. CSLI Publications, Stanford (1995)
Mohanan, T.: Arguments in Hindi. Center for the Study of Language and Information, Leland Stanford Junior University, United States (1994)
Butt, M.: Conscious Choice And Some Light Verbs In Urdu. In: Verma, M.K. (ed.) Manohar (1993)
Butt, M.: Complex Predicates Compendium, Tromso (May 2005)
Agnihotri, R.K.: Hindi, An Essential Grammar, pp. 121–126. Routledge, London (2007)
Bahl, K.C.: Studies in the Semantic Structure of Hindi. Motilal Banarasidass, Bihar (1974)
Bharati, A., Chaitanya, V., Sangal, R.: Natural Language Processing: A Paninian Perspective, pp. 65–106. Prentice Hall of India, New Delhi (1995)
Begum, R., Husain, S., Dhwaj, A., Sharma, D.M., Bai, L., Sangal, R.: Dependency Annotation Scheme for Indian Languages. In: The Third International Joint Conference on Natural Language Processing (IJCNLP), Hyderabad, India (2008)
Bhatt, R., Narasimhan, B., Palmer, M., Rambow, O., Sharma, D.M., Xia, F.: A Multi Representational and MultiLayered Treebank for Hindi/Urdu. In: The Third Linguistic Annotation Workshop (The LAW III) in Conjunction with ACL/IJCNLP, Singapore (2009)
Kellogg, S.H.R.: A Grammar of the Hindi Language. Routledge and Kegan Paul Ltd., London (1875)
Bailey, T.G.: Teach Yourself Urdu. In: Firth, J.R., Harley, A.H. (eds.), London (1956)
Guru, K.P.: Hindi Vyakarana. Kasi Nagari Pracarini Sabha, Kasi (1922)
Sharma, A.: A Basic Grammar of Modern Hindi. Government of India, Ministry of Education and Scientific Research, Delhi (1958)
Bahri, H.: Hindi Semantics. The Bharati Press, Allahabad (1959)
Bahl, K.C.: A Reference Grammar of Hindi. The University of Chicago, Chicago (1967)
Bhattacharyya, P., Chakrabarti, D., Sarma, V.M.: Complex predicates in Indian languages and wordnets. Language Resources and Evaluation 40(3-4), 331–355 (2006)
Mukerjee, A., Soni, A., Raina, A.M.: Detecting Complex Predicates in using POS Projection across parallel Corpora Aligned Hindi sentence. In: Workshop on Multiword Expressions, Sydney, pp. 11–18 (2006)
Sinha, R.M.K.: Mining Complex Predicates In Hindi Using A Parallel Hindi-English Corpus. In: ACL International Joint Conference in Natural Language Processing, p. 40 (2009)
Venkatapathy, S., Joshi, A.: Relative Compositionality    of   Noun+Verb Multi-word Expressions in Hindi. In: ICON (2005)
Chakrabarti, D., Mandalia, H., Priya, R., Sarma, V., Bhattacharyya, P.: Hindi Compound Verbs and their Automatic Extraction. In: International Conference on Computational Linguistics, pp. 27–30 (2008)
Ratnaparkhi, A.: Maximum Entropy Models for Natural Language Ambiguity Resolution. Ph.D. thesis (1998)
Rosenfeld, R.: Adaptive Statistical Language Modeling: A Maximum Entropy Approach. Ph.D. thesis, Carnegie Mellon University (1994)
Chen, S.F., Rosenfeld, R.: A Gaussian prior for smoothing maximum entropy models. Technical Report CMUCS-99-108, Carnegie Mellon University (1999)
Beeferman, D., Berger, A., Lafferty, J.: Statistical models for text segmentation. Machine Learning 34(1-3), 177–210 (1999)
Ratnaparkhi, A., Reynar, J., Roukos, S.: A maximum entropy model for prepositional phrase attachment. In: ARPA Human Language Technology Workshop, pp. 250–255 (1994)
Ratnaparkhi, A.: A maximum entropy model for part-of-speech tagging. In: Empirical Methods in Natural Language Conference (1996)
Nigam, K., Lafferty, J., McCallum, A.: Using Maximum Entropy for Text Classification. In: IJCAI 1999 Workshop on Machine Learning for Information Filtering, pp. 61–67 (1999)
Berger, A., Pietra, D.: A Maximum Entropy Approach to Natural Language Processing. Computational Linguistics, Vol 22, 39–71 (1996)
Bharati, A., Husain, S., Vijay, M., Deepak, K., Sharma, D.M., Sangal, R.: Constraint Based Hybrid Approach to Parsing Indian Languages. In: The 23rd Pacific Asia Conference on Language, Information and Computation (PACLIC 23), Hong Kong (2009)
Gadde, P., Jindal, K., Husain, S., Sharma, D.M., Sangal, R.: Improving Data Driven Dependency Parsing using Clausal Information. In: NAACL-HLT 2010, Los Angeles, CA (2010)
Nivre, J., Hall, J., Kubler, S., McDonald, R., Nilsson, J., Riedel, S., Yuret, D.: The CoNLL 2007 Shared Task on Dependency Parsing. In: CoNLL Shared Task Session of EMNLP-CoNLL (2007)
Ambati, B.R., Husain, S., Nivre, J., Sangal, R.: On the Role of Morphosyntactic Features in Hindi Dependency Parsing. In: NAACL-HLT 2010 Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL 2010), Los Angeles, CA (2010)
Ambati, B.R., Gade, P., GSK, C.: Effect of Minimal Semantics on Dependency Parsing. In: RANLP 2009 Student Research Workshop (2009)
McDonald, R., Pereira, F., Ribarov, K., Hajic, J.: Non-projective dependency parsing using spanning tree algorithms. In: HLT/EMNLP, pp. 523–530 (2005)
McDonald, R., Crammer, K., Pereira, F.: On-line large-margin training of dependency parsers. In: ACL 2005, pp. 91–98 (2005)
Ambati, B.R., Gadde, P., Jindal, K.: Experiments in Indian Language Dependency Parsing. In: ICON 2009 NLP Tools Contest: Indian Language Dependency Parsing, pp. 32–37 (2009)
Husain, S., Mannem, P., Ambati, B., Gadde, P.: The ICON-2010 tools contest on Indian language dependency parsing. In: ICON 2010 Tools Contest on Indian Language Dependency Parsing, Kharagpur, India (2010)
Husain, S.: Dependency Parsers for Indian Languages. In: ICON 2009 NLP Tools Contest: Indian Language Dependency Parsing, Hyderabad, India (2009)
Bharati, A., Husain, S., Ambati, B., Jain, S., Sharma, D., Sangal, R.: Two Semantic features make all the difference in Parsing accuracy. In: International Conference on Natural Language Processing (2008)
Buchholz, S., Marsi, E.: CoNLL-X shared task on multilingual dependency parsing. In: Tenth Conference on Computational Natural Language Learning (CoNLL), pp. 149–164 (2006)
Ambati, B.R., Husain, S., Jain, S., Sharma, D.M., Sangal, R.: Two methods to incorporate local morphosyntactic features in Hindi dependency parsing. In: NAACL-HLT 2010 Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL 2010), Los Angeles, CA (2010)
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Begum, R., Jindal, K., Jain, A., Husain, S., Misra Sharma, D. (2011). Identification of Conjunct Verbs in Hindi and Its Effect on Parsing Accuracy. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_3
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