A Supervised Learning Approach to Biological Question Answering
學年 98
學期 1
出版（發表）日期 2009-08-01
作品名稱 A Supervised Learning Approach to Biological Question Answering
作品名稱（其他語言）
著者 戴敏育; Day, Min-Yuh; Lin, Ryan T. K.; Chiu, Justin Liang-te; Dai, Hong-jie; Tsai, Richard Tzong-han; Hsu, Wen-lian
單位 淡江大學資訊管理學系
出版者
著錄名稱、卷期、頁數 Integrated Computer-Aided Engineering 16(3), pp.271-281
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.
關鍵字
語言 en
ISSN 1069-2509 1875-8835
期刊性質 國外
收錄於
產學合作
通訊作者
審稿制度 否
國別 NLD
公開徵稿
出版型式 ,電子版,紙本
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