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Recognizing Question Entailment for Medical Question Answering.

Asma Ben Abacha1, Dina Demner-Fushman1

  • 1U.S. National Library of Medicine, Bethesda, MD.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|March 9, 2017
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This study introduces a new method for finding similar medical questions using Recognizing Question Entailment (RQE). This approach helps automatically answer consumer health questions by matching them to existing expert-answered FAQs.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Health Information Retrieval

Background:

  • Medical texts are increasingly specialized, making automated question answering difficult.
  • Retrieving similar, expert-answered questions is a promising strategy for medical QA.

Purpose of the Study:

  • To develop a novel approach for detecting similar questions using Recognizing Question Entailment (RQE).
  • To automatically match consumer health questions to existing Frequently Asked Questions (FAQs) for answering.

Main Methods:

  • Utilized Recognizing Question Entailment (RQE) for similar question detection.
  • Leveraged Frequently Asked Questions (FAQs) as a source of expert-answered information.
  • Evaluated the approach using consumer health questions from the National Library of Medicine and NIH FAQs.

Main Results:

  • The proposed RQE-based method shows promising initial results for identifying similar questions.
  • The approach demonstrates feasibility in complementing traditional question answering systems.

Conclusions:

  • The RQE approach is a viable method for enhancing medical question answering by utilizing FAQs.
  • This technique offers a valuable addition to existing automated question answering solutions in healthcare.