{"title":"Chinese Word Segmentation as LMR Tagging","authors":"Nianwen Xue, Libin Shen","doi":"10.3115/1119250.1119278","DOIUrl":null,"url":null,"abstract":"In this paper we present Chinese word segmentation algorithms based on the so-called LMR tagging. Our LMR taggers are implemented with the Maximum Entropy Markov Model and we then use Transformation-Based Learning to combine the results of the two LMR taggers that scan the input in opposite directions. Our system achieves F-scores of 95.9% and 91.6% on the Academia Sinica corpus and the Hong Kong City University corpus respectively.","PeriodicalId":403123,"journal":{"name":"Workshop on Chinese Language Processing","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"151","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Chinese Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3115/1119250.1119278","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 151
Abstract
In this paper we present Chinese word segmentation algorithms based on the so-called LMR tagging. Our LMR taggers are implemented with the Maximum Entropy Markov Model and we then use Transformation-Based Learning to combine the results of the two LMR taggers that scan the input in opposite directions. Our system achieves F-scores of 95.9% and 91.6% on the Academia Sinica corpus and the Hong Kong City University corpus respectively.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
