Transition based parser for telugu language

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    This paper explains transition dependency parsing approaches to build a dependency parser for Telugu language. Telugu treebank is given as an input to transition dependency parsers. One of the best transition dependency parser is the Malt parser. It is an independent system and it has nine methods to parse a sentence of any language. We have applied the treebank on all the methods of a Malt parser among which Arc-eager parser produces state-of-art results for Telugu language. Arc-eager method was produced LA (Label Accuracy) of 63%, UAS (Unlabeled Attachment Score) of 88.1% and LAS (Label Attachment Score) of 62.3%. In this paper we discuss a brief introduction of all Malt Parsing methods and an in detail explanation of Arc-eager dependency parsing.

     

     


     

  • Keywords


    Dependency Parsing; Telugu Language; Transition Based Parsing; Malt Parser; Arc-Eager Parsing.

  • References


      1. [1] Nivre, J. (2009) Parsing Indian Languages with MaltParser. In Proceedings of the ICON09 NLP Tools Contest: Indian Language Dependency Parsing, 12-18.

        [2] Joakim Nivre, Johan Hall, Jens Nilsson, Atanas Chanev, Gülsen Eryiğit, Sandra Kübler, Svetoslav Marinov, and Erwin Marsi. MaltParser: A language-independent system for data-driven dependency parsing. Natural Language Engineering, 13(2):95-135, 2007.

        [3] Joakim Nivre, Ryan McDonald (2011) Analyzing and integrating dependency parsers. Computational Linguistics, 37(1):197-230, 2011. https://doi.org/10.1162/coli_a_00039.

        [4] Yoav Goldberg and Joakim Nivre. A dynamic oracle for arc-eager dependency parsing. In Proceedings of the 24th International Conference on Computational Linguistics (COLING), pages 959-976, 2012.

        [5] Covington, M. A. 2001. A fundamental algorithm for dependency parsing. In Proceedings of the 39th Annual ACM Southeast Conference, pp. 95–102.

        [6] G. Nagaraju, N. Mangathayaru, B. Padmaja Rani. Dependency Parser for Telugu language. In Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, Article No. 138, 2016. https://doi.org/10.1145/2905055.2905354.

        [7] Ryo Nagata, Keisuke Sakaguchi. Phrase Structured Annotation and Parsing for Learner English. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 1837-1847, 2016.

        [8] Akshar Bharati, Mridul Gupta, Vineet Yadav, Karthik Gali and Dipti Misra Sharma. Simple Parser for Indian Languages in a Dependency Framework. In Proceedings of the Third Linguistic Annotation Workshop, ACL-IJCNLP, 162-165, 2009.

        [9] Martins, N. Smith, E. Xing, Concise Integer Linear Programming for Dependency Parsing. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the fourth International Joint Conference on Natural Language Processing of the AFNLP, pp. 342-350.

        [10] Goldberg, Y., Elhadad, M., 2010a. Easy first dependency parsing of Modern Hebrew. In: Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically Rich Languages. SPMRL ’10. Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 103–107.

        [11] Y. Zhang, S. Clark, Syntactic processing using the generalized perceptron and beam search Computational Linguistics, 37 (1) (Mar. 2011), pp. 105-151. https://doi.org/10.1162/coli_a_00037.

        [12] B. Venkata Seshu Kumari, Ramisetty Rajeshwara Rao, Telugu dependency parsing using different statistical parsers. Journal of King Saud University-Computer and Information Sciences.2017, pp. 134-140.

        [13] J. Eisner. 1996. Three new probabilistic models for depedency parsing. An exploration. In Proc. COLING.

        [14] Y.J. Chu and T.H. Liu. 1965. on the shortest arborescence of a directed graph. Science Sinica, 14:1396-1400.

        [15] J. Edmonds. 1967. Optimum branches. Journal of Research of the National Bureau of Standards, 71B:233-240. https://doi.org/10.6028/jres.071B.032.

        [16] Ryan McDonald, Fernando Pereira, Kiril Ribarov, and Jan Hajic. 2005b. Non-projective dependency parsing using spanning tree algorithms. In Proceedings of HLT/EMNLP-2005. Vancouver, Canada, pages 523-530.

        [17] Ryan McDonald and Fernando Pereira 2006. Online Learning of Approximate Dependency Parsing Algorithms. In Proceedings of 11th International Conference of the European Chapter of the Association for Computational Linguistics.

        [18] Joakim Nivre, Johan Hall, and Jens Nillson. 2006. Malt Parser: A data-driven parser-generator for dependency parsing. In proc. of LREC.

        [19] Ryan McDonald, Koby Crammer, and Fernando Pereira. 2005. Online large-margin training of dependency parsers. In Proc of ACL. https://doi.org/10.3115/1219840.1219852.

        [20] Kenji Sagae and Alon Lavie. 2006b. Parser combination by reparsing. In Proc of NAACL. https://doi.org/10.3115/1614049.1614082.

        [21] Yoav Goldburg and Michael Elhabad. An Efficient Algorithm for Easy-First Non-Directional Dependency Parsing. The 2010 Annual Conference of the North American Chapter of the ACL, Pages 742-750, Los Angles, California, June 2010.


 

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Article ID: 14015
 
DOI: 10.14419/ijet.v7i4.14015




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