Paraphrase Substitution for Recognizing Textual Entailment

We describe a method for recognizing textual entailment that uses the length of the longest common subsequence (LCS) between two texts as its decision criterion. Rather than requiring strict word matching in the common subsequences, we perform a flexible match using automatically generated paraphrases. We find that the use of paraphrases over strict word matches represents an average F-measure improvement from 0.22 to 0.36 on the CLEF 2006 Answer Validation Exercise for 7 languages. Categories and Subject Descriptors I.2.7 [Artificial Intelligence]: Natural Language Processing--Language parsing and understanding General Terms Experimentation, Languages, Reliability Keywords Recognizing textual entailment, Paraphrase generation, Question answering

• Type: Conference
• Year: 2006
• Where: CLEF
• Authors: Wauter Bosma, Chris Callison-Burch
• Keywords: CLEF 2006, Common Subsequences, Information Management, Strict Word, Textual Entailment