A statistics-based semantic textual entailment system

Authors:
Partha Pakray;Utsab Barman;Sivaji Bandyopadhyay;Alexander Gelbukh
Affiliations:
Computer Science and Engineering Department, Jadavpur University, Kolkata, India;Computer Science and Engineering Department, Jadavpur University, Kolkata, India;Computer Science and Engineering Department, Jadavpur University, Kolkata, India;Center for Computing Research, National Polytechnic Institute, Mexico City, Mexico
Venue:
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Year:
2011

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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.