{"title":"基于规则的汉语分句标点处理方法","authors":"Jing Wang, Yun Zhu, Yaohong Jin","doi":"10.1109/IALP.2014.6973504","DOIUrl":null,"url":null,"abstract":"In this paper, a rule-based sentence segmentation system is proposed. We studied the usage and function of Chinese punctuation marks, and classified them into 4 categories. According to whether punctuation can split a sentence, we tagged it with a label SST or un-SST. Experiments were conducted on 4 different kinds of corpus containing 12 kinds of Chinese punctuation marks, and our model achieves a high F-measure over 90% overall. Experiment results show that our approach is effectively for sentence segmentation.","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A rule-based method for Chinese punctuations processing in sentences segmentation\",\"authors\":\"Jing Wang, Yun Zhu, Yaohong Jin\",\"doi\":\"10.1109/IALP.2014.6973504\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a rule-based sentence segmentation system is proposed. We studied the usage and function of Chinese punctuation marks, and classified them into 4 categories. According to whether punctuation can split a sentence, we tagged it with a label SST or un-SST. Experiments were conducted on 4 different kinds of corpus containing 12 kinds of Chinese punctuation marks, and our model achieves a high F-measure over 90% overall. Experiment results show that our approach is effectively for sentence segmentation.\",\"PeriodicalId\":117334,\"journal\":{\"name\":\"2014 International Conference on Asian Language Processing (IALP)\",\"volume\":\"258 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Asian Language Processing (IALP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IALP.2014.6973504\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Asian Language Processing (IALP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2014.6973504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A rule-based method for Chinese punctuations processing in sentences segmentation
In this paper, a rule-based sentence segmentation system is proposed. We studied the usage and function of Chinese punctuation marks, and classified them into 4 categories. According to whether punctuation can split a sentence, we tagged it with a label SST or un-SST. Experiments were conducted on 4 different kinds of corpus containing 12 kinds of Chinese punctuation marks, and our model achieves a high F-measure over 90% overall. Experiment results show that our approach is effectively for sentence segmentation.
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