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Analyzing the Text of Clinical Literature for Question Answering

Analyzing the Text of Clinical Literature for Question Answering

Yun Niu, Graeme Hirst
Copyright: © 2009 |Pages: 31
ISBN13: 9781605662749|ISBN10: 1605662747|ISBN13 Softcover: 9781616925284|EISBN13: 9781605662756
DOI: 10.4018/978-1-60566-274-9.ch011
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MLA

Niu, Yun, and Graeme Hirst. "Analyzing the Text of Clinical Literature for Question Answering." Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, IGI Global, 2009, pp. 190-220. https://doi.org/10.4018/978-1-60566-274-9.ch011

APA

Niu, Y. & Hirst, G. (2009). Analyzing the Text of Clinical Literature for Question Answering. In V. Prince & M. Roche (Eds.), Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration (pp. 190-220). IGI Global. https://doi.org/10.4018/978-1-60566-274-9.ch011

Chicago

Niu, Yun, and Graeme Hirst. "Analyzing the Text of Clinical Literature for Question Answering." In Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, 190-220. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-274-9.ch011

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Abstract

The task of question answering (QA) is to find an accurate and precise answer to a natural language question in some predefined text. Most existing QA systems handle fact-based questions that usually take named entities as the answers. In this chapter, the authors take clinical QA as an example to deal with more complex information needs. They propose an approach using Semantic class analysis as the organizing principle to answer clinical questions. They investigate three Semantic classes that correspond to roles in the commonly accepted PICO format of describing clinical scenarios. The three Semantic classes are: the description of the patient (or the problem), the intervention used to treat the problem, and the clinical outcome. The authors focus on automatic analysis of two important properties of the Semantic classes.

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