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首页> 外文会议>International Conference on Asian Digital Libraries(ICADL 2004); 20041213-17; Shanghai(CN) >Semantic Query Expansion Based on a Question Category Concept List
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Semantic Query Expansion Based on a Question Category Concept List

机译：基于问题类别概念列表的语义查询扩展

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摘要

When confronted with a query, question answering systems endeavor to extract the most exact answers possible by determining the answer type that fits with the query and the key terms used in the query. However, the efficacy of such systems is limited by the fact that the terms used in a query may be in a syntactic form different to that of the same words in a document. In this paper, we present an efficient semantic query expansion methodology based on a question category concept list comprised of terms that are semantically close to terms used in a query. The semantically close terms of a term in a query may be hypernyms, synonyms, or terms in a different syntactic category. The proposed system first constructs a concept list for each question type and then builds the concept list for each question category using a learning algorithm. When a new query is given, the question is classified into the node in question category, and the query is expanded using the concept list of the classified category. In the question answering experiments on 42,654 Wall Street Journal documents of the TREC collection, the traditional system showed in 0.223 in MRR and the proposed system showed 0.50 superior to the traditional question answering system. The results of the present experiments suggest the promise of the proposed method.
机译：当遇到查询时，问题回答系统会通过确定适合查询的答案类型和查询中使用的关键术语来努力提取最准确的答案。但是，此类系统的功效受到以下事实的限制：查询中使用的术语可能采用与文档中相同单词不同的句法形式。在本文中，我们提出了一个有效的语义查询扩展方法，该方法基于问题类别概念列表，该列表由语义上接近查询中所用术语的术语组成。查询中某个术语的语义上接近的术语可以是上位词，同义词或不同句法类别中的术语。所提出的系统首先为每种问题类型构建概念列表，然后使用学习算法为每种问题类别构建概念列表。当给出新查询时，将问题分类为问题类别中的节点，并使用分类类别的概念列表扩展查询。在对TREC馆藏的42654份《华尔街日报》文档进行的问答实验中，传统系统的MRR显示为0.223，而建议的系统则比传统问答系统高出0.50。本实验的结果表明了该方法的前景。

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