{"title":"基于文本的数据挖掘","authors":"Chris Clifton, Rick Steinheiser","doi":"10.1109/CMPSAC.1998.716738","DOIUrl":null,"url":null,"abstract":"Data mining technology is giving us the ability to extract meaningful patterns from large quantities of structured data. Information retrieval systems have made large quantities of textual data available. Extracting meaningful patterns from this data is difficult. Current tools for mining structured data are inappropriate for free text. We outline problems involved in Knowledge Discovery in Text, and present an architecture for extracting patterns that hold across multiple documents. The capabilities that such a system could provide are illustrated.","PeriodicalId":252030,"journal":{"name":"Proceedings. The Twenty-Second Annual International Computer Software and Applications Conference (Compsac '98) (Cat. No.98CB 36241)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Data mining on text\",\"authors\":\"Chris Clifton, Rick Steinheiser\",\"doi\":\"10.1109/CMPSAC.1998.716738\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data mining technology is giving us the ability to extract meaningful patterns from large quantities of structured data. Information retrieval systems have made large quantities of textual data available. Extracting meaningful patterns from this data is difficult. Current tools for mining structured data are inappropriate for free text. We outline problems involved in Knowledge Discovery in Text, and present an architecture for extracting patterns that hold across multiple documents. The capabilities that such a system could provide are illustrated.\",\"PeriodicalId\":252030,\"journal\":{\"name\":\"Proceedings. The Twenty-Second Annual International Computer Software and Applications Conference (Compsac '98) (Cat. No.98CB 36241)\",\"volume\":\"62 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. The Twenty-Second Annual International Computer Software and Applications Conference (Compsac '98) (Cat. No.98CB 36241)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CMPSAC.1998.716738\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. The Twenty-Second Annual International Computer Software and Applications Conference (Compsac '98) (Cat. No.98CB 36241)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CMPSAC.1998.716738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data mining technology is giving us the ability to extract meaningful patterns from large quantities of structured data. Information retrieval systems have made large quantities of textual data available. Extracting meaningful patterns from this data is difficult. Current tools for mining structured data are inappropriate for free text. We outline problems involved in Knowledge Discovery in Text, and present an architecture for extracting patterns that hold across multiple documents. The capabilities that such a system could provide are illustrated.