Problem Content Table Construction Based on Extracting Sym-Multi-Word-Co from Texts
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.
KeywordsMulti-word co-occurrence Verb phrase Symptom content
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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