{"title":"混合中文文本分块","authors":"PanPan Liao, Y. Liu, Lin Chen","doi":"10.1109/IRI.2006.252475","DOIUrl":null,"url":null,"abstract":"Text chunking is an effective method to decrease the difficulty of natural language parsing. In this paper, a statistical method based on hidden Markov model (HMM) is used for Chinese text chunking. Moreover, a transformation based error-driven learning approach is adopted to improve the performance. The definition of transformation rule templates is the key problem of this machine learning approach. All the templates are learned from the corpus automatically in this paper. The precision using HMM is 88.19% and the precision is 92.67% combining HMM and transformation based error-driven learning","PeriodicalId":402255,"journal":{"name":"2006 IEEE International Conference on Information Reuse & Integration","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Hybrid Chinese Text Chunking\",\"authors\":\"PanPan Liao, Y. Liu, Lin Chen\",\"doi\":\"10.1109/IRI.2006.252475\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Text chunking is an effective method to decrease the difficulty of natural language parsing. In this paper, a statistical method based on hidden Markov model (HMM) is used for Chinese text chunking. Moreover, a transformation based error-driven learning approach is adopted to improve the performance. The definition of transformation rule templates is the key problem of this machine learning approach. All the templates are learned from the corpus automatically in this paper. The precision using HMM is 88.19% and the precision is 92.67% combining HMM and transformation based error-driven learning\",\"PeriodicalId\":402255,\"journal\":{\"name\":\"2006 IEEE International Conference on Information Reuse & Integration\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE International Conference on Information Reuse & Integration\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRI.2006.252475\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Conference on Information Reuse & Integration","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI.2006.252475","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Text chunking is an effective method to decrease the difficulty of natural language parsing. In this paper, a statistical method based on hidden Markov model (HMM) is used for Chinese text chunking. Moreover, a transformation based error-driven learning approach is adopted to improve the performance. The definition of transformation rule templates is the key problem of this machine learning approach. All the templates are learned from the corpus automatically in this paper. The precision using HMM is 88.19% and the precision is 92.67% combining HMM and transformation based error-driven learning
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