{"title":"基于最大熵法的汉语词性标注","authors":"Hong Ling, Chun-Fa Yuan","doi":"10.1109/ICMLC.2002.1167446","DOIUrl":null,"url":null,"abstract":"A lot of researches have been made on the application of the maximum entropy modeling in natural language processing in recent years. In this paper, we present a new Chinese part of speech tagging method based on the maximum entropy principle because Chinese language is quite different from many other languages. The feature selection is the key point in our system, which is distinct from the one used in English. Experiment results show that the part of speech tagging accuracy ratio of our system is up to 97.34%.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"367 1","pages":"1447-1450 vol.3"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Chinese part of speech tagging based on maximum entropy method\",\"authors\":\"Hong Ling, Chun-Fa Yuan\",\"doi\":\"10.1109/ICMLC.2002.1167446\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A lot of researches have been made on the application of the maximum entropy modeling in natural language processing in recent years. In this paper, we present a new Chinese part of speech tagging method based on the maximum entropy principle because Chinese language is quite different from many other languages. The feature selection is the key point in our system, which is distinct from the one used in English. Experiment results show that the part of speech tagging accuracy ratio of our system is up to 97.34%.\",\"PeriodicalId\":90702,\"journal\":{\"name\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"volume\":\"367 1\",\"pages\":\"1447-1450 vol.3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2002.1167446\",\"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. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1167446","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Chinese part of speech tagging based on maximum entropy method
A lot of researches have been made on the application of the maximum entropy modeling in natural language processing in recent years. In this paper, we present a new Chinese part of speech tagging method based on the maximum entropy principle because Chinese language is quite different from many other languages. The feature selection is the key point in our system, which is distinct from the one used in English. Experiment results show that the part of speech tagging accuracy ratio of our system is up to 97.34%.
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