UB.dmirg: Learning Textual Entailment Relationships Using Lexical Semantic Features
نویسندگان
چکیده
This paper describes our Recognizing Textual Entailment (RTE) system developed at University of Ballarat, Australia for participation in the Text Analysis Conference RTE 2010 competition. This year, we participated in the Main task and used a machine learning approach for learning textual entailment relationships using parse-free lexical semantic features. For this, we employed FrameNet and WordNet resources to extract event-based and semantic features from both hypotheses and texts. Our system also used the longest common substring of lemmas when learning the entailment relationships.
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تاریخ انتشار 2010