{"title":"基于字符和词判别模型的汉语分词和词性标注联合译码","authors":"Xinxin Li, Xuan Wang, Lin Yao","doi":"10.1109/IALP.2011.24","DOIUrl":null,"url":null,"abstract":"For Chinese word segmentation and POS tagging problem, both character-based and word-based discriminative approaches can be used. Experiments show that these two approaches bring different errors and can complement each other. In this paper, we propose a joint decoding model based on both character-based and word-based models using multi-beam search algorithm. Experimental results show that the joint decoding model outperforms character-based and word-based baseline models.","PeriodicalId":297167,"journal":{"name":"2011 International Conference on Asian Language Processing","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Joint Decoding for Chinese Word Segmentation and POS Tagging Using Character-Based and Word-Based Discriminative Models\",\"authors\":\"Xinxin Li, Xuan Wang, Lin Yao\",\"doi\":\"10.1109/IALP.2011.24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For Chinese word segmentation and POS tagging problem, both character-based and word-based discriminative approaches can be used. Experiments show that these two approaches bring different errors and can complement each other. In this paper, we propose a joint decoding model based on both character-based and word-based models using multi-beam search algorithm. Experimental results show that the joint decoding model outperforms character-based and word-based baseline models.\",\"PeriodicalId\":297167,\"journal\":{\"name\":\"2011 International Conference on Asian Language Processing\",\"volume\":\"88 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Asian Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IALP.2011.24\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Asian Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2011.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Joint Decoding for Chinese Word Segmentation and POS Tagging Using Character-Based and Word-Based Discriminative Models
For Chinese word segmentation and POS tagging problem, both character-based and word-based discriminative approaches can be used. Experiments show that these two approaches bring different errors and can complement each other. In this paper, we propose a joint decoding model based on both character-based and word-based models using multi-beam search algorithm. Experimental results show that the joint decoding model outperforms character-based and word-based baseline models.