Passage Retrieval Using Answer Type Profiles in Question Answering

Retrieving answer containing passages is a challenging task in Question Answering. In this paper, we describe a novel passage retrieval methodology using answer type profiles. Our methodology includes two steps: estimation and ranking. In the estimation step, answer type profiles are constructed from question-answer sentence pairs parallel corpus using a statistical alignment model. Each answer type profile consists of triples: the query word, the answering sentence word and the probability of translation. In the ranking step, answer type profiles are incorporated into the Language Modeling framework called Statistical Machine Translation models for Information Retrieval. Using this framework a set of relevant passages are retrieved, given a question. We conducted experiments on FACTOID questions from TREC 2002 to 2006 QA tracks. The experimental results showed significant improvements over different retrieval models including TFIDF, Okapi BM25, Indri and KL-divergence.