Using the Web as Corpus for Un-supervised Learning in Question Answering

In this paper we propose a method for unsupervised learning of relation between terms in questions and answer passages by using the Web as corpus. The method involves automatic acquisition of relevant answer passages from the Web for a set of questions and answers, as well as alignment of wh -phrases and keywords in questions with phrases in the answer passages. At run time, wh-phrases and keywords are transformed to a sequence of expanded query terms in order to bias the underlying search engine to give higher rank to relevant passages. Evaluation on a set of questions shows that our prototype improves the performance of a question answering system by increasing the precision rate of top ranking passages returned by the search engine.