Abstract
This paper aims to work on the Question Answering (QA) system within online web boards, especially the Why-question, How-question, and Request-Diagnosis-question types approach for solving problems. The research QA system benefits for the online communities in solving their problems, especially on health-care problems of symptoms. Both question and answer expressions are based on multiple EDUs (Elementary Discourse Units) where each EDU is equivalent to a simple sentence or a clause. The research involves two main problems: how to identify the question types of Why, How, and Request-Diagnosis and how to determine the corresponding answer from the knowledge source after solving the question focuses. Thus, the research applies different machine learning techniques, Naïve Bayes and Support Vector Machine, to solve the reasoning question type identification. The knowledge source contains several symptom-treatment vector pairs and several cause-effect vector pairs. Therefore, we propose clustering symptoms/problems of the knowledge source before determining an answer based on top-down levels of determining similarity scores between a web board question and the knowledge source. The research achieves 83 % correctness of the answer determination with potentially saving amounts of search time.
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References
Aloise, D., Deshpande, A., Hansen, P., Popat, P.: NP-hardness of Euclidean sum of squares clustering. Mach. Learn. 75, 245–249 (2009)
Baral, C., Vo, N.H., Liang, S.: Answering why and how questions with respect to a frame-based knowledge base: a preliminary report. In: Proceedings of ICLP 2012, Hungary (2012)
Biggins, S., Mohammed, S., Oakley, S.: University of Sheffield: two approaches to semantic text similarity. In: Proceedings of First Joint Conference on Lexical and Computational Semantics, Montréal, Canada (2012)
Carlson, L., Marcu, D., Okurowski, M.E.: Building a discourse-tagged corpus in the framework of rhetorical structure theory. Curr. New Dir. Discourse Dialogue 22, 85–112 (2003)
Chanlekha, H., Kawtrakul, A.: Thai named entity extraction by incorporating maximum entropy model with simple heuristic information. In: IJCNLP’ 2004 Proceedings (2004)
Chareonsuk, J., Sukvakree, T., Kawtrakul, A.: Elementary discourse unit segmentation for Thai using discourse cue and syntactic information. In: NCSEC 2005 Proceedings (2005)
Girju, R.: Automatic detection of causal relations for question answering. In: Proceedings of 41st Annual Meeting of the Association for Computational Linguistics, Workshop on Multilingual Summarization and Question Answering-Machine Learning and Beyond, Japan (2003)
Mitchell, T.M.: Machine Learning. The McGraw-Hill Companies Inc. and MIT Press, Singapore (1997)
Oh, J-H., Torisawa, K., Hashimoto, C., Sano, M., Saeger, S.D., Ohtake, K.: Why-Question answering using intra- and inter-sentential causal relations. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Bulgaria (2013)
Pechsiri, C., Moolwat, O., Piriyakul, R.: Symptom-treatment relation extraction from web-documents for construct know-how map. In: KICSS’ 2013 Proceedings (2013)
Pechsiri, C., Piriyakul, R.: Explanation knowledge graph construction through causality extraction from texts. J. Comput. Sci. Technol. 25(5), 1055–1070 (2010)
Schwitter, R., Rinaldi, F., Clematide, S.: The importance of how-questions in technical domains. In: Proceedings of TALN-04, Workshop Question—Réponse, Fez, Morocco (2004)
Sudprasert, S., Kawtrakul, A.: Thai word segmentation based on global and local unsupervised learning. In: NCSEC’2003 Proceedings (2003)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, USA (1995)
Verberne, S., Boves, L., Coppen, P.-A., Oostdijk, N.: Discourse-based answering of Why-Questions. Traitement Automatique des Langues 47, 2 (2007)
Acknowledgments
This is supported by Thai Research Fund (MRG5580030). The medical-care knowledge and the pharmacology knowledge applied in this research are provided by Prof. Puangthong Kraipiboon, a clinician of Division of Medical Oncology, Department of Medicine, Ramathibodi Hospital, and Uraiwan Janviriyasopak, a pharmacist of RexPharmcy, respectively.
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Pechsiri, C., Moolwat, O., Piriyakul, R. (2016). Web Board Question Answering System on Problem-Solving Through Problem Clusters. In: Kunifuji, S., Papadopoulos, G., Skulimowski, A., Kacprzyk , J. (eds) Knowledge, Information and Creativity Support Systems. Advances in Intelligent Systems and Computing, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-319-27478-2_12
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DOI: https://doi.org/10.1007/978-3-319-27478-2_12
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