Intention Classification for Retrieval of Health Questions

Authors

  • Rey-Long Liu Department of Medical Informatics, Tzu Chu University, Taiwan.

Keywords:

Health Questions, Intention Recognition, Text Classification, Learning-Based Classifiers, Location-based Feature Weighting, Area-based Feature Weighting

Abstract

Healthcare professionals have edited many health questions (HQs) and their answers for healthcare consumers on the Internet. The HQs provide both readable and reliable health information, and hence retrieval of those HQs that are relevant to a given question is essential for health education and promotion through the Internet. However, retrieval of relevant HQs needs to be based on the recognition of the intention of each HQ, which is difficult to be done by predefining syntactic and semantic rules. We thus model the intention recognition problem as a text classification problem, and develop two techniques to improve a learning-based text classifier for the problem. The two techniques improve the classifier by location-based and area-based feature weightings, respectively. Experimental results show that, the two techniques can work together to significantly improve a Support Vector Machine classifier in both the recognition of HQ intentions and the retrieval of relevant HQs. 

URL: http://ijkcdt.net/xml/09914/09914.pdf

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Published

2017-03-31

How to Cite

Liu, R.-L. (2017). Intention Classification for Retrieval of Health Questions. International Journal of Knowledge Content Development & Technology, 7(1). Retrieved from https://ijkcdt.journals.publicknowledgeproject.org/index.php/ijkcdt/article/view/109

Issue

Vol. 7 No. 1 (2017)

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Articles

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Copyright (c) 2017 International Journal of Knowledge Content Development & Technology

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