{"title":"使用改进的最小置信度采样策略的主动学习方法用于命名实体识别","authors":"Ankit Agrawal, Sarsij Tripathi, M. Vardhan","doi":"10.1007/s13748-021-00230-w","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":46027,"journal":{"name":"Progress in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s13748-021-00230-w","citationCount":"9","resultStr":"{\"title\":\"Active learning approach using a modified least confidence sampling strategy for named entity recognition\",\"authors\":\"Ankit Agrawal, Sarsij Tripathi, M. Vardhan\",\"doi\":\"10.1007/s13748-021-00230-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":46027,\"journal\":{\"name\":\"Progress in Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2021-01-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1007/s13748-021-00230-w\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Progress in Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s13748-021-00230-w\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Progress in Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13748-021-00230-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
期刊介绍:
This journal publishes top-level research results in all aspects of artificial intelligence, with a particular emphasis on the following topics: data mining; soft computing and computational intelligence; knowledge, complexity, logic, planning, reasoning and search; agents and multiagent systems; artificial vision and robotics; and natural language and Web intelligence.