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    题名: A Statistical Approach with Syntactic and Semantic Features for Chinese Textual Entailment
    作者: Tu, Chun;Day, Min-yuh
    贡献者: 淡江大學資訊管理學系
    关键词: Textual Entailment;Semantic Features;Syntactic Features;Machine Learning;Support Vector Machine (SVM)
    日期: 2012-08-08
    上传时间: 2012-10-19
    出版者: IEEE Press
    摘要: 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.
    關聯: Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2012), pp.59-64
    DOI: 10.1109/IRI.2012.6302991
    显示于类别:[資訊管理學系暨研究所] 會議論文


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