Empirical Analysis of Context Sensitive Grammars and Parse Trees for Disambiguiting Telugu Language Sentences

Authors

  • Jinka Sreedhar  Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technoloy, Hyderabad, India
  • SK Althaf Hussain Basha  Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technoloy, Hyderabad, India
  • D. Praveen Kumar  Department of Computer Science and Engineering Institute of Technology, Dhanbad, Jharkhand, India
  • A. Jagan  Department of Computer Science and Engineering B.V.Raju Institute of Technology, Narsapur, Telangana, India
  • Baijnath Kaushik  Department of Computer Science and Engineering Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India

Keywords:

Natural Language Processing (NLP), Context Sensitive Grammars (CSG) Rules, Parse Trees(PT’s), Derivations.

Abstract

This research paper explores the impact of Context Sensitive Grammars (CSG) and Parse Trees for construction of a Telugu Language Sentences. Based on the CSG Rules here we derived the derivations for the respective strings. Later we constructed the Parser Trees for the above said strings. Finally we analysed whether the string is ambiguous or unambiguous. Here for analysis we considered the Large Scale Open Source Telugu carpus. The main aim of finding out these methods, is to find out solution to the problem of ambiguity in Telugu Language Sentences. Here designing of Context Sensitive Grammars Rules and Parse Trees are explained with examples.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-09-30

Issue

Volume 2, Issue 7, September-2017

Section

Research Articles

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How to Cite

[1]
Jinka Sreedhar, SK Althaf Hussain Basha, D. Praveen Kumar, A. Jagan, Baijnath Kaushik, " Empirical Analysis of Context Sensitive Grammars and Parse Trees for Disambiguiting Telugu Language Sentences, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.328-331, September-2017.