{"title":"用于健康问题诊断的文本多词共现集","authors":"Onuma Moolwat, C. Pechsiri","doi":"10.1109/EAIS.2015.7368786","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":325875,"journal":{"name":"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-Word-Co-occurrence collection from texts for health-problem diagnosis\",\"authors\":\"Onuma Moolwat, C. Pechsiri\",\"doi\":\"10.1109/EAIS.2015.7368786\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":325875,\"journal\":{\"name\":\"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EAIS.2015.7368786\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EAIS.2015.7368786","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.