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    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/58504

    題名: A Supervised Learning Approach to Biological Question Answering
    作者: 戴敏育;Day, Min-Yuh;Lin, Ryan T. K.;Chiu, Justin Liang-te;Dai, Hong-jie;Tsai, Richard Tzong-han;Hsu, Wen-lian
    貢獻者: 淡江大學資訊管理學系
    日期: 2009-08
    上傳時間: 2011-10-01 12:05:21 (UTC+8)
    摘要: Biologists rely on keyword-based search engines to retrieve superficially relevant papers, from which they must filter out the irrelevant information manually. Question answering (QA) systems can offer more efficient and user-friendly ways of retrieving such information. Two contributions are provided in this paper. First, a factoid QA system is developed to employ a named entity recognition module to extract answer candidates and a linear model to rank them. The linear model uses various semantic features, such as named entity types and semantic roles. To tune the weights of features used by the model, a novel supervised learning algorithm, which only needs small amounts of training data, is provided. Second, a QA system may assign several answers with the same score, making evaluation unfair. To solve this problem, an efficient formula for a mean average reciprocal rank (MARR) is proposed to reduce the complexity of its computation. After employing all effective semantic features, our system achieves a top-1 MARR of 74.11% and top-5 MARR of 76.68%. In comparison of the baseline system, the top-1 and top-5 MARR increase by 9.5% and 7.1%. In addition, the experiment result on test set shows our ranking method, which achieves 55.58% top-1 MARR and 66.99% top-5 MARR, significantly surpasses traditional BM25 and simple voting in performance by averagely 35.23% and 36.64%, respectively.
    關聯: Integrated Computer-Aided Engineering 16(3), pp.271-281
    DOI: 10.3233/ICA-2009-0316
    顯示於類別:[資訊管理學系暨研究所] 期刊論文


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