Question answering (QA) is the task of retrieving an answer in response to a question by analyzing documents. Although most of the efforts in developing QA systems are devoted to dealing with electronic text, we consider it is also necessary to develop systems for document images. In this paper, we propose a method of document image retrieval for such QA systems. Since the task is not to retrieve all relevant documents but to find the answer somewhere in documents, retrieval should be precision oriented. The main contribution of this paper is to propose a method of improving precision of document image retrieval by taking into account the co-occurrence of successive terms in a question. The indexing scheme is based on two-dimensional distributions of terms and the weight of co-occurrence is measured by calculating the density distributions of terms. The proposed method was tested by using 1253 pages of documents about the major league baseball with 20 questions and found that it is superior to the baseline method proposed by the authors.

Document retrieval is a fundamental but important task for intelligent access to a huge amount of information stored in documents. Although the history of its research is long, it is still a hard task especially in the case that lengthy documents are retrieved with very short queries (a few keywords). For the retrieval of long documents, methods called passage-based document retrieval have proven to be effective. In this paper, we experimentally show that a passage-based method based on window passages is also effective for dealing with short queries on condition that documents are not too short. We employ a method called "density distributions" as a method based on window passages, and compare it with three conventional methods: the simple vector space model, pseudo relevance feedback and latent semantic indexing. We also compare it with a passage-based method based on discourse passages.