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versión On-line ISSN 1870-9044
Polibits no.43 México ene./jun. 2011
Semantic Textual Entailment Recognition using UNL
Partha Pakray1*, Soujanya Poria1**, Sivaji Bandyopadhyay1***, and Alexander Gelbukh2
2 Faculty of Law, Waseda University, Tokyo, Japan, on Sabbatical leave from the Center for Computing Research, National Polytechnic Institute, Mexico City, Mexico (email: email@example.com).
Manuscript received November 2, 2010.
Manuscript accepted for publication January 12, 2011.
A twoway textual entailment (TE) recognition system that uses semantic features has been described in this paper. We have used the Universal Networking Language (UNL) to identify the semantic features. UNL has all the components of a natural language. The development of a UNL based textual entailment system that compares the UNL relations in both the text and the hypothesis has been reported. The semantic TE system has been developed using the RTE3 test annotated set as a development set (includes 800 texthypothesis pairs). Evaluation scores obtained on the RTE4 test set (includes 1000 texthypothesis pairs) show 55.89% precision and 65.40% recall for YES decisions and 66.50% precision and 55.20% recall for NO decisions and overall 60.3% precision and 60.3% recall.
Key words: Textual Entailment, Universal Networking Language (UNL), RTE3 Test Annotated Data, RTE4 Test Data
This work was supported in part by the Government of India and Government of Mexico (joint DSTCONACYT project) and Government of Mexico (CONACYT 50206H, SIPIPN 20113295, as well as SNI and CONACYT Sabbatical program as to the fourth author).
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