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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/987654321/58507

    題名: Boosting Chinese Question Answering with Two Lightweight Methods: ABSPs and SCO-QAT
    作者: 戴敏育;Day, Min-yuh;Lee, Cheng-wei;Sung, Cheng-lung;Lee, Yi-hsun;Jiang, Tian-jian;Wu, Chia-wei;Shih, Cheng-wei;Chen, Yu-ren;Hsu, Wen-lian
    貢獻者: 淡江大學資訊管理學系
    日期: 2008-11
    上傳時間: 2011-10-01 12:05:49 (UTC+8)
    摘要: Question Answering (QA) research has been conducted in many languages. Nearly all the top performing systems use heavy methods that require sophisticated techniques, such as parsers or logic provers. However, such techniques are usually unavailable or unaffordable for under-resourced languages or in resource-limited situations. In this article, we describe how a top-performing Chinese QA system can be designed by using lightweight methods effectively. We propose two lightweight methods, namely the Sum of Co-occurrences of Question and Answer Terms (SCO-QAT) and Alignment-based Surface Patterns (ABSPs). SCO-QAT is a co-occurrence-based answer-ranking method that does not need extra knowledge, word-ignoring heuristic rules, or tools. It calculates co-occurrence scores based on the passage retrieval results. ABSPs are syntactic patterns trained from question-answer pairs with a multiple alignment algorithm. They are used to capture the relations between terms and then use the relations to filter answers. We attribute the success of the ABSPs and SCO-QAT methods to the effective use of local syntactic information and global co-occurrence information.

    By using SCO-QAT and ABSPs, we improved the RU-Accuracy of our testbed QA system, ASQA, from 0.445 to 0.535 on the NTCIR-5 dataset. It also achieved the top 0.5 RU-Accuracy on the NTCIR-6 dataset. The result shows that lightweight methods are not only cheaper to implement, but also have the potential to achieve state-of-the-art performances.
    關聯: ACM Transactions on Asian Language Information Processing 7(4)
    DOI: 10.1145/1450295.1450297
    顯示於類別:[資訊管理學系暨研究所] 期刊論文


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