结构化地使用外部知识进行基于事件的开放领域问答

G. Yang, Tat-Seng Chua, Shuguang Wang, Chun-Keat Koh
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引用次数: 117

摘要

问答(QA)中的一个主要问题是,查询要么太简短,要么经常不包含目标语料库中最相关的术语。为了克服这个问题,我们早期的工作集成了从Web和WordNet中提取的外部知识,对TREC-11任务执行基于事件的QA。本文通过揭示外部知识中的结构,扩展了我们执行基于事件的QA的方法。该知识结构松散地建模了质量保证事件的不同方面,并与连续约束松弛算法相结合,实现了有效的质量保证。我们在TREC-11 QA语料库上获得的结果表明,新方法更有效,能够获得80%以上的置信度加权分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structured use of external knowledge for event-based open domain question answering
One of the major problems in question answering (QA) is that the queries are either too brief or often do not contain most relevant terms in the target corpus. In order to overcome this problem, our earlier work integrates external knowledge extracted from the Web and WordNet to perform Event-based QA on the TREC-11 task. This paper extends our approach to perform event-based QA by uncovering the structure within the external knowledge. The knowledge structure loosely models different facets of QA events, and is used in conjunction with successive constraint relaxation algorithm to achieve effective QA. Our results obtained on TREC-11 QA corpus indicate that the new approach is more effective and able to attain a confidence-weighted score of above 80%.
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