MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies
MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies
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DOI：
10.1016/j.ipm.2015.04.006
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发表时间：
2015-09-01
影响因子：
8.6
通讯作者：
Zweigenbaum, Pierre
中科院分区：
文献类型：
--
作者：
Ben Abacha, Asma;Zweigenbaum, Pierre
问答（QA）任务旨在从文档集合或数据库中为用户问题提供精确和快速的答案。随着数字信息的急剧增长，对这种红外系统的需求越来越大。在本文中，我们解决的问题，QA在医疗领域的几个特定的条件得到满足。我们提出了一种语义方法QA的基础上（一）自然语言处理技术，它允许深入分析的医疗问题和文件，（二）语义Web技术在代表和审讯水平。我们提出了我们的语义检索系统，称为MEANS和我们提出的方法“答案搜索”的基础上语义搜索和查询放松。我们评估了从MEDLINE文章中提取的真实的问题和答案的整体系统性能。我们的实验显示了有希望的结果，并建议查询放松策略可以进一步提高整体性能。(C)2015爱思唯尔有限公司版权所有。
The Question Answering (QA) task aims to provide precise and quick answers to user questions from a collection of documents or a database. This kind of IR system is sorely needed with the dramatic growth of digital information. In this paper, we address the problem of QA in the medical domain where several specific conditions are-met. We propose a semantic approach to QA based on (i) Natural Language Processing techniques, which allow a deep analysis of medical questions and documents and (ii) semantic Web technologies at both representation and interrogation levels. We present our Semantic Question-Answering System, called MEANS and our proposed method for "Answer Search" based on semantic search and query relaxation. We evaluate the overall system performance on real questions and answers extracted from MEDLINE articles. Our experiments show promising results and suggest that a query-relaxation strategy can further improve the overall performance. (C) 2015 Elsevier Ltd. All rights reserved.