Abstract
This research aims to construct a problem content table, particularly health-problem/symptom contents from downloaded health-care documents. The content table includes Disease Name, Symptom Concept, Symptom-Location Concept, and Sym-Multi-Word-Co Expression (which is a multi-word co-occurrence having a symptom concept on verb phrases). The research results benefit for a diagnosis system. The research has four problems; how to identify Sym-Multi-Word-Co from verb phrases, how to determine Sym-Multi-Word-Co boundaries after stemming words and eliminating stop words, how to solve Sym-Multi-Word-Co ambiguities, and how to derive symptom concepts and location concepts from Sym-Multi-Word-Co expressions with implicit-symptom-location occurrences. Therefore, we apply the symptom-verb-concept set to identify Sym-Multi-Word-Co and also to solve the Sym-Multi-Word-Co ambiguities. We also propose Bayesian Network to solve the Sym-Multi-Word-Co boundaries. We apply WordNet and MeSH to derive symptom concepts and implicit-symptom-location concepts. The research results provide the symptom content table with the high precision of the Sym-Multi-Word-Co extraction from the documents.
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Acknowledgement
This work has been supported by the Department of Information Technology, Dhurakij Pundit University, Thailand. Moreover, Onuma Moolwat, Achara, and Uraiwan Janviriyasopak have contributed greatly in this research.
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Pechsiri, C. (2019). Problem Content Table Construction Based on Extracting Sym-Multi-Word-Co from Texts. In: Theeramunkong, T., et al. Advances in Intelligent Informatics, Smart Technology and Natural Language Processing. iSAI-NLP 2017. Advances in Intelligent Systems and Computing, vol 807. Springer, Cham. https://doi.org/10.1007/978-3-319-94703-7_21
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DOI: https://doi.org/10.1007/978-3-319-94703-7_21
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