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Document Image Retrieval for QA Systems Based on the Density Distributions of Successive Terms
Koichi KISE Shota FUKUSHIMA Keinosuke MATSUMOTO
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2005/08/01
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Document Image Understanding and Digital Documents)
Category: Document Image Retrieval
document image retrieval, density distribution, precision, question-answering,
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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.