A Supervised Learning Approach to Biological Question Answering

doi:10.3233/ICA-2009-0316

機構典藏 > College of Business and Management > Graduate Institute & Department of Information Management > Journal Article > Item 987654321/58504

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/58504

Title:	A Supervised Learning Approach to Biological Question Answering
Authors:	戴敏育;Day, Min-Yuh;Lin, Ryan T. K.;Chiu, Justin Liang-te;Dai, Hong-jie;Tsai, Richard Tzong-han;Hsu, Wen-lian
Contributors:	淡江大學資訊管理學系
Date:	2009-08
Issue Date:	2011-10-01 12:05:21 (UTC+8)
Abstract:	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.
Relation:	Integrated Computer-Aided Engineering 16(3), pp.271-281
DOI:	10.3233/ICA-2009-0316
Appears in Collections:	[Graduate Institute & Department of Information Management] Journal Article

Files in This Item:

File	Size	Format
index.html	0Kb	HTML	150	View/Open

Loading...