Lenz Furrer, S. Küker, J. Berezowski, H. Posthaus, F. Vial, Fabio Rinaldi
{"title":"构建面向兽医文本挖掘的症候术语资源","authors":"Lenz Furrer, S. Küker, J. Berezowski, H. Posthaus, F. Vial, Fabio Rinaldi","doi":"10.5167/UZH-114496","DOIUrl":null,"url":null,"abstract":"Public health surveillance systems rely on the automated monitoring of large amounts of text. While building a text mining system for veterinary syndromic surveillance, we exploit automatic and semi-automatic methods for terminology construction at different stages. Our approaches include term extraction from free-text, grouping of term variants based on string similarity, and linking to an existing medical ontology.","PeriodicalId":269925,"journal":{"name":"International Conference on Terminology and Artificial Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Constructing a Syndromic Terminology Resource for Veterinary Text Mining\",\"authors\":\"Lenz Furrer, S. Küker, J. Berezowski, H. Posthaus, F. Vial, Fabio Rinaldi\",\"doi\":\"10.5167/UZH-114496\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Public health surveillance systems rely on the automated monitoring of large amounts of text. While building a text mining system for veterinary syndromic surveillance, we exploit automatic and semi-automatic methods for terminology construction at different stages. Our approaches include term extraction from free-text, grouping of term variants based on string similarity, and linking to an existing medical ontology.\",\"PeriodicalId\":269925,\"journal\":{\"name\":\"International Conference on Terminology and Artificial Intelligence\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Terminology and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5167/UZH-114496\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Terminology and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5167/UZH-114496","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Constructing a Syndromic Terminology Resource for Veterinary Text Mining
Public health surveillance systems rely on the automated monitoring of large amounts of text. While building a text mining system for veterinary syndromic surveillance, we exploit automatic and semi-automatic methods for terminology construction at different stages. Our approaches include term extraction from free-text, grouping of term variants based on string similarity, and linking to an existing medical ontology.
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