MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies

被引：128

作者：

Ben Abacha, Asma ^{[1
]}

Zweigenbaum, Pierre ^{[2
]}

机构：

[1] LIST, L-1855 Kirchberg, Luxembourg

[2] LIMSI CNRS, Paris, France

来源：

INFORMATION PROCESSING & MANAGEMENT | 2015年 / 51卷 / 05期

关键词：

Question answering; Natural language processing; Semantic search; Medical informatics; INFORMATION; DOCTORS;

D O I：

10.1016/j.ipm.2015.04.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：570 / 594

页数：25

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