{"title":"基于LMR标注的汉语分词","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":"{\"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}","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}
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.
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