用于健康问题诊断的文本多词共现集

Onuma Moolwat, C. Pechsiri
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引用次数: 0

摘要

本研究旨在搜集与健康问题/症状概念相关的多字共现词,用于健康问题诊断。本研究的结果对于帮助普通人对健康问题进行初步诊断具有一定的意义。本研究的多词模式是基于动词短语的事件表达。然而,该研究存在两个主要问题;第一个问题是如何识别多词共现,包括多词共现边界和停用词去除后的症状概念。二是歧义的多词共现概念。因此,利用Naïve贝叶斯的机器学习来解决动词短语(停止词消除后)的词尾词作为与症状概念的多词共现。本研究结果可提供高准确度的多词共现症状概念判定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Word-Co-occurrence collection from texts for health-problem diagnosis
This research aims to collect multi-word co-occurrences with health-problem/symptom concepts for health-problem diagnosis from wed-board documents. The result of this research is a benefit for assisting the ordinary people in preliminary diagnosis health problems. The multi-Word-Co of the research is based on an event expression by a verb phrase. However, the research contains two main problems; the first problem is how to identify multi-word co-occurrence including the multi-word co-occurrence boundary with the symptom concept after the stop word removal. The second one is the ambiguous multi-word co-occurrence concept. Therefore, the machine learning with Naïve Bayes is applied to solve the consequent words of the verb phrase (after the stop word elimination) as the multi-word co-occurrence with the symptom concept. The results of this research can provide the high precision of the symptom concept determination through multiword co-occurrences on documents.
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