Passage Retrieval Based on Density Distributions of Terms and Its Applications to Document Retrieval and Question Answering

A huge amount of electronic documents has created the demand of intelligent access to their information. Document retrieval has been investigated for providing a fundamental toot for the demand. However, it is not satisfactory due to (1) inaccuracies of retrieving long documents with short queries (a few terms), (2) a user's burden on finding relevant parts from retrieved long documents. In this paper, we apply a passage retrieval method called "density distributions" (DD) to tackle these problems. For the first problem, it is experimentally shown that a passage-based method outperforms conventional document retrieval methods if long documents are retrieved with short queries. For the second problem, we apply DD to the question answering task: locating short passages in response to natural language queries of seeking facts. Preliminary experiments show that correct answers can be located within a window of 50 terms for about a half of such queries.