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Work on the semantics of questions has argued that the relation between a question and its answer(s) can be cast in terms of logical entailment. In this paper, we demonstrate how computational systems designed to recognize textual entailment can be used to enhance the accuracy of current open-domain automatic question answering (Q/A) systems. | Methods for Using Textual Entailment in Open-Domain Question Answering Sanda Harabagiu and Andrew Hickl Language Computer Corporation 1701 North Collins Boulevard Richardson Texas 75080 USA sanda@languagecomputer.com Abstract Work on the semantics of questions has argued that the relation between a question and its answer s can be cast in terms of logical entailment. In this paper we demonstrate how computational systems designed to recognize textual entailment can be used to enhance the accuracy of current open-domain automatic question answering Q A systems. In our experiments we show that when textual entailment information is used to either filter or rank answers returned by a Q A system accuracy can be increased by as much as 20 overall. 1 Introduction Open-Domain Question Answering Q A systems return a textual expression identihed from a vast document collection as a response to a question asked in natural language. In the quest for producing accurate answers the open-domain Q A problem has been cast as 1 a pipeline of linguistic processes pertaining to the processing of questions relevant passages and candidate answers interconnected by several types of lexico-semantic feedback cf. Harabagiu et al. 2001 Moldovan et al. 2002 2 a combination of language processes that transform questions and candidate answers in logic representations such that reasoning systems can select the correct answer based on their proofs cf. Moldovan et al. 2003 3 a noisy-channel model which selects the most likely answer to a question cf. Echi-habi and Marcu 2003 or 4 a constraint satisfaction problem where sets of auxiliary questions are used to provide more information and better constrain the answers to individual questions cf. Prager et al. 2004 . While different in their approach each of these frameworks seeks to approximate the forms of semantic inference that will allow them to identify valid textual answers to natural language questions. Recently the task of automatically .
