A Statistical Approach with Syntactic and Semantic Features for Chinese Textual Entailment

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Title:	A Statistical Approach with Syntactic and Semantic Features for Chinese Textual Entailment
Authors:	Tu, Chun;Day, Min-yuh
Contributors:	淡江大學資訊管理學系
Keywords:	Textual Entailment;Semantic Features;Syntactic Features;Machine Learning;Support Vector Machine (SVM)
Date:	2012-08-08
Issue Date:	2012-10-19
Publisher:	IEEE Press
Abstract:	Recognizing Textual Entailment (RTE) is a PASCAL/TAC task in which two text fragments are processed by system to determine whether the meaning of hypothesis is entailed from another text or not. In this paper, we proposed a textual entailment system using a statistical approach that integrates syntactic and semantic techniques for Recognizing Inference in Text (RITE) using the NTCIR-9 RITE task and make a comparison between semantic and syntactic features based on their differences. We thoroughly evaluate our approach using subtasks of the NTCIR-9 RITE. As a result, our system achieved 73.28% accuracy on the Chinese Binary-Class (BC) subtask with NTCIR-9 RITE. Thorough experiments with the text fragments provided by the NTCIR-9 RITE task show that the proposed approach can significantly improve system accuracy.
Relation:	Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2012), pp.59-64
DOI:	10.1109/IRI.2012.6302991
Appears in Collections:	[Graduate Institute & Department of Information Management] Proceeding

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