Recognizing Textual Entailment Based on Inference Phenomena
REN Han1,2, FENG Wenhe1,2, LIU Maofu2,3, WAN Jing2
1. Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou, Guangdong 510006, China; 2. Hubei Research Center for Language and Intelligent Information Processing, Wuhan University, Wuhan, Hubei 430072,China; 3. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei 430065, China; 4. Center for Lexicographical Studies, Guangdong University of Foreign Studies, Guangzhou, Guangdoing 510420, China
Abstract：This paper introduces an approach of textual entailment recognition based on language phenomena. The approach asopts a joint classification model for language phenomenon recognition and entailment recognition, so as to learn two highly relevant tasks, avoiding error propagation in pipeline strategy. For language phenomenon recognition, 22 specific and 20 general features are employed. And for enhancing the generalization of random forest, a feature selection method is adopted on building trees of random forest. Experimental results show that the joint classification model based on random forest recognizes language phenomena and entailment relation effectively.
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2017, Vol. 31 
任函(1980—),博士,助理研究员,主要研究领域为自然语言处理。E-mail: hanren@whu.edu.cn
冯文贺(1976—),通信作者,博士,讲师,主要研究领域为理论语言学、计算语言学。E-mail: wenhefeng@gmail.com
刘茂福(1977—),博士,教授,主要研究领域为自然语言处理。E-mail: liumaofu@wust.edu.cn