Experiments on POS tagging and data driven dependency parsing for Telugu language

  • Authors:

  • Mayana Humera Khanam;Palli Suryachandra;Kv Madhumurthy

  • Affiliations:

  • S V University, Tirupathi, India;S V University, Tirupathi, India;S V University, Tirupathi, India

  • Venue:
  • Proceedings of the International Conference on Advances in Computing, Communications and Informatics
  • Year:
  • 2012

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Abstract

In this paper we present our experiments on Part-Of-Speech tagging and data driven dependency Parsing for Telugu language. We adopted three Part-Of-Speech taggers named as Brill tagger, Maximum Entropy tagger and Trigrams 'n' Tags tagger (TnT) to Telugu language and compares their performance. TnT tagger has showed better accuracy for Telugu. We used T'nT tagger for assigning the Part- Of-Speech tags and chunks for developing the annotated data for Dependency parsing. Telugu Language is morphologically rich free-word order language. We did experiments on two data-driven parsers Malt and MST for Telugu language and compare results of both the parsers. We describe the data and parser settings used in detail. We are also presented, which parser gives best results for different sentence types in Telugu.