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Document Image Retrieval in a Question Answering System for Document Images

机译:文档图像检索在文档图像的问题应答系统中

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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-occurrences of .successive terms in a question. The indexing scheme in 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 in superior to the baseline method proposed by the authors.
机译:问题回答(QA)是通过分析回复答案的答案的任务;文件。虽然开发QA的大部分努力都致力于处理电子文本,但我们认为还有必要为文档图像开发系统,在本文中,我们提出了一种对此类QA的文档图像检索方法。由于任务不检索所有相关文件但是。要找到答案。在文件中的位置,检索应精确定向。本文的主要贡献是提出通过考虑到。在问题中的共同发生的情况下提出提高文件图像检索精度的方法。通过计算术语的密度分布来测量基于术语二维分布的索引方案和共发生的重量。通过关于主要联赛棒球的1253页文件测试了该方法,并发现了20个问题,发现了;它优于作者提出的基线方法。

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