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    题名: Chinese Textual Entailment with Wordnet Semantic and Dependency Syntactic Analysis
    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;Dependency Analysis;WordNet;Syntactic Features;Machine Learning;Support Vector Machine (SVM)
    日期: 2013-08-14
    上传时间: 2013-10-18 05:24:17 (UTC+8)
    出版者: IEEE Press
    摘要: Recognizing Inference in TExt (RITE) is a task for automatically detecting entailment, paraphrase, and contradiction in texts which addressing major text understanding in information access research areas. In this paper, we proposed a Chinese textual entailment system using Wordnet semantic and dependency syntactic approaches in Recognizing Inference in Text (RITE) using the NTCIR-10 RITE-2 subtask datasets. Wordnet is used to recognize entailment at lexical level. Dependency syntactic approach is a tree edit distance algorithm applied on the dependency trees of both the text and the hypothesis. We thoroughly evaluate our approach using NTCIR-10 RITE-2 subtask datasets. As a result, our system achieved 73.28% on Traditional Chinese Binary-Class (BC) subtask and 74.57% on Simplified Chinese Binary-Class subtask with NTCIR-10 RITE-2 development datasets. Thorough experiments with the text fragments provided by the NTCIR-10 RITE-2 subtask showed that the proposed approach can improve system's overall accuracy.
    關聯: Proceedings of the 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI), pp.69-74
    显示于类别:[資訊管理學系暨研究所] 會議論文


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