Question Answering as a Classification Task
نویسندگان
چکیده
In this paper we treat question answering (QA) as a classification problem. Our motivation is to build systems for many languages without the need for highly tuned linguistic modules. Consequently, word tokens and web data are used extensively but no explicit linguistic knowledge is incorporated. A mathematical model for answer retrieval, answer classification and answer length prediction is derived. The TREC 2002 QA task is used for system development where a confidence weighted score (CWS) of 0.551 is obtained. Performance is evaluated on the factoid questions of the TREC 2003 QA task where a CWS of 0.419 is obtained which is in the mid-range of contemporary QA systems on the same task.
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تاریخ انتشار 2005