{"title":"MPLUS:概率医学语言理解系统","authors":"Lee M. Christensen, P. Haug, M. Fiszman","doi":"10.3115/1118149.1118154","DOIUrl":null,"url":null,"abstract":"This paper describes the basic philosophy and implementation of MPLUS (M+), a robust medical text analysis tool that uses a semantic model based on Bayesian Networks (BNs). BNs provide a concise and useful formalism for representing semantic patterns in medical text, and for recognizing and reasoning over those patterns. BNs are noise-tolerant, and facilitate the training of M+.","PeriodicalId":339993,"journal":{"name":"ACL Workshop on Natural Language Processing in the Biomedical Domain","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"108","resultStr":"{\"title\":\"MPLUS: a probabilistic medical language understanding system\",\"authors\":\"Lee M. Christensen, P. Haug, M. Fiszman\",\"doi\":\"10.3115/1118149.1118154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes the basic philosophy and implementation of MPLUS (M+), a robust medical text analysis tool that uses a semantic model based on Bayesian Networks (BNs). BNs provide a concise and useful formalism for representing semantic patterns in medical text, and for recognizing and reasoning over those patterns. BNs are noise-tolerant, and facilitate the training of M+.\",\"PeriodicalId\":339993,\"journal\":{\"name\":\"ACL Workshop on Natural Language Processing in the Biomedical Domain\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"108\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACL Workshop on Natural Language Processing in the Biomedical Domain\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3115/1118149.1118154\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACL Workshop on Natural Language Processing in the Biomedical Domain","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3115/1118149.1118154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MPLUS: a probabilistic medical language understanding system
This paper describes the basic philosophy and implementation of MPLUS (M+), a robust medical text analysis tool that uses a semantic model based on Bayesian Networks (BNs). BNs provide a concise and useful formalism for representing semantic patterns in medical text, and for recognizing and reasoning over those patterns. BNs are noise-tolerant, and facilitate the training of M+.
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