Abstract
Healthcare consumers often access the Internet to get health information related to specific health questions, which are often about several health categories such as the cause, diagnosis, and process (e.g., treatment) of disorders. Therefore, for a given health question q, a classifier should be developed to recognize the intended category (or categories) of q so that relevant information specifically for answering q can be retrieved. In this paper, we show that a Support Vector Machine (SVM) classifier can be trained to properly classify real-world Chinese health questions (CHQs), and more importantly by weighting the words in the CHQs based on their locations in the CHQs, the SVM classifier can be further improved significantly. The improved classifier can serve as a fundamental component to retrieve relevant health information from health information websites, as well as the collections of CHQs whose answers have been written by healthcare professionals so that healthcare consumers can get reliable health information, which is particularly essential in health promotion and disease management.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abbas, J., Schwartz, D.G., Krause, R.: Emergency Medical Residents’ Use of Google® for Answering Clinical Questions in the Emergency Room. In: Proc. of ASIST 2010 (2010)
Boguski, M.S.: Online Health Information Retrieval by Consumers and the Challenge of Personal Genomics. In: Willard, H.F., Ginsburg, G.S. (eds.) Genomic and Personalized Medicine, vol. 1-2 (2009)
Casellas, N., Casanovas, P., Vallbé, J.-J., Poblet, M., Blázquez, M., Contreras, J., López-Cobo, J.-M., Richard, V.: Semantic Enhancement for Legal Information Retrieval: IURISERVICE performance. In: Proceedings of ICAIL, Palo Alto, CA USA (2007)
Eysenbach, G., Köhler, C.: How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews. British Medical Journal 324, 573–577 (2002)
Joachims, T.: Making Large-Scale SVM Learning Practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT-Press (1999)
Lee, C.-W., Day, M.-Y., Sung, C.-L., Lee, Y.-H., Jiang, T.-J., Wu, C.-W., Shih, C.-W., Chen, Y.-R., Hsu, W.-L.: Boosting Chinese Question Answering with Two Lightweight Methods: ABSPs and SCO-QAT. ACM Trans. Asian Lang. Inform. Process Article 12 (2008)
Lin, J., Demner-Fushman, D.: The Role of Knowledge in Conceptual Retrieval: A Study in the Domain of Clinical Medicine. In: Proceedings of SIGIR 2006, Seattle, Washington, USA (2006)
Liu, R.-L.: A Passage Extractor for Classification of Disease Aspect Information. Journal of the American Society for Information Science and Technology 64(11), 2265–2277 (2013)
Liu, R.-L., Lin, S.-L.: A Conceptual Model for Retrieval of Chinese Frequently Asked Questions in Healthcare. In: Hou, Y., Nie, J.-Y., Sun, L., Wang, B., Zhang, P. (eds.) AIRS 2012. LNCS, vol. 7675, pp. 366–375. Springer, Heidelberg (2012)
Liszka, H.A., Steyer, T.E., Hueston, W.J.: Virtual Medical Care: How Are Our Patients Using Online Health Information? Journal of Community Health 31(5), 368–378 (2006)
Lv, Y., Zhai, C.: Positional Language Models for Information Retrieval. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 299–306 (2009)
Shuyler, K.S., Knight, K.M.: What Are Patients Seeking When They Turn to the Internet? Qualitative Content Analysis of Questions Asked by Visitors to an Orthopaedics Web Site. Journal of Medical Internet Research 5(4), e24 (2003)
Wang, K., Ming, Z., Chua, T.-S.: A Syntactic Tree Matching Approach to Finding Similar Questions in Community-based QA Services. In: Proceedings of SIGIR 2009, Boston, Massachusetts, USA (2009)
Wu, C.-H., Yeh, J.-F., Lai, Y.-S.: Semantic Segment Extraction and Matching for Internet FAQ Retrieval. IEEE Transactions on Knowledge and Data Engineering 18(7) (2006)
Wu, C.-H., Yeh, J.-F., Chen, M.-J.: Domain-Specific FAQ Retrieval Using Independent Aspects. ACM Transactions on Asian Language Information Processing 4(1), 1–17 (2005)
Zeng, Q.T., Kogan, S., Plovnick, R.M., Crowell, J., Lacroix, E.-M., Greenes, R.A.: Positive attitudes and failed queries: an exploration of the conundrums of consumer health information retrieval. International Journal of Medical Informatics 73, 45–55 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Liu, RL. (2014). Improving Health Question Classification by Word Location Weights. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-05476-6_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05475-9
Online ISBN: 978-3-319-05476-6
eBook Packages: Computer ScienceComputer Science (R0)