A Statistics-Based Semantic Textual Entailment System

  • Partha Pakray
  • Utsab Barman
  • Sivaji Bandyopadhyay
  • Alexander Gelbukh
Conference paper

DOI: 10.1007/978-3-642-25324-9_23

Volume 7094 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Pakray P., Barman U., Bandyopadhyay S., Gelbukh A. (2011) A Statistics-Based Semantic Textual Entailment System. In: Batyrshin I., Sidorov G. (eds) Advances in Artificial Intelligence. MICAI 2011. Lecture Notes in Computer Science, vol 7094. Springer, Berlin, Heidelberg

Abstract

We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entailment decision. The system has been separately trained on each development corpus released as part of the Recognizing Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets.

Keywords

textual entailment Universal Networking Language Recognizing Textual Entailment data sets 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Partha Pakray
    • 1
  • Utsab Barman
    • 1
  • Sivaji Bandyopadhyay
    • 1
  • Alexander Gelbukh
    • 2
  1. 1.Computer Science and Engineering DepartmentJadavpur UniversityKolkataIndia
  2. 2.Center for Computing ResearchNational Polytechnic InstituteMexico CityMexico