MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies. Issue 5 (September 2015)

• Record Type: Journal Article
• Authors: Ben Abacha, Asma; Zweigenbaum, Pierre
• Is Part Of: Information processing & management. Volume 51:Issue 5(2015:Sep.)
• Page Start: 570
• Page End: 594
• Publication Date: 2015-09
• Subjects: Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2015.04.006
• Languages: English
• ISSNs: 0306-4573

Abstract

<abstract xml:lang="en" abstract-type="author-highlights" id="ab005"> <title id="st190">Highlights</title> <sec> <p id="sp005"> <list id="l0005"> <list-item id="u0005"> <label></label> <p id="p0710">We propose a semantic question answering system (MEANS) for the medical domain.</p> </list-item> <list-item id="u0010"> <label></label> <p id="p0715">We introduce a novel query relaxation approach for question answering.</p> </list-item> <list-item id="u0015"> <label></label> <p id="p0720">MEANS integrates NLP methods allowing a deep analysis of questions and documents.</p> </list-item> <list-item id="u0020"> <label></label> <p id="p0725">MEANS uses semantic Web technologies and standards for data sharing and integration.</p> </list-item> <list-item id="u0025"> <label></label> <p id="p0730">Our experiments show promising results in terms of MRR and precision.</p> </list-item> </list> </p> </sec> </abstract>