Open AccessTelugu dependency parsing using different statistical parsers
Author(s) -
B. Venkata Seshu Kumari,
R. Rajeshwara Rao
Publication year - 2015
Publication title -
journal of king saud university - computer and information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 33
eISSN - 2213-1248
pISSN - 1319-1578
DOI - 10.1016/j.jksuci.2014.12.006
Subject(s) - telugu , computer science , dependency grammar , parsing , dependency (uml) , artificial intelligence , natural language processing , feature (linguistics) , philosophy , linguistics
In this paper we explore different statistical dependency parsers for parsing Telugu. We consider five popular dependency parsers namely, MaltParser, MSTParser, TurboParser, ZPar and Easy-First Parser. We experiment with different parser and feature settings and show the impact of different settings. We also provide a detailed analysis of the performance of all the parsers on major dependency labels. We report our results on test data of Telugu dependency treebank provided in the ICON 2010 tools contest on Indian languages dependency parsing. We obtain state-of-the art performance of 91.8% in unlabeled attachment score and 70.0% in labeled attachment score. To the best of our knowledge ours is the only work which explored all the five popular dependency parsers and compared the performance under different feature settings for Telugu
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