期刊論文
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 |
相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/58504 ) |