{"title":"中文NP组块:一种半监督方法","authors":"Yen-Hsi Lin, Zhao-Ming Gao","doi":"10.1109/ISUC.2008.62","DOIUrl":null,"url":null,"abstract":"V N and N V sequence in Chinese may be a noun phrase. This characteristic makes NP chunking in Chinese particularly difficult. We present a method to tackle this problem by combining Chinese Sinica Treebank data with unlabelled data to train a better model based on SVM. Experiments with open test data show that our proposed semi-supervised approach can achieve the accuracy of 78.79% in f-measure, enhancing the f-measure by 8.79% over the supervised approach.","PeriodicalId":339811,"journal":{"name":"2008 Second International Symposium on Universal Communication","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Chinese NP Chunking: A Semi-Supervised Approach\",\"authors\":\"Yen-Hsi Lin, Zhao-Ming Gao\",\"doi\":\"10.1109/ISUC.2008.62\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"V N and N V sequence in Chinese may be a noun phrase. This characteristic makes NP chunking in Chinese particularly difficult. We present a method to tackle this problem by combining Chinese Sinica Treebank data with unlabelled data to train a better model based on SVM. Experiments with open test data show that our proposed semi-supervised approach can achieve the accuracy of 78.79% in f-measure, enhancing the f-measure by 8.79% over the supervised approach.\",\"PeriodicalId\":339811,\"journal\":{\"name\":\"2008 Second International Symposium on Universal Communication\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Second International Symposium on Universal Communication\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISUC.2008.62\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Second International Symposium on Universal Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISUC.2008.62","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
V N and N V sequence in Chinese may be a noun phrase. This characteristic makes NP chunking in Chinese particularly difficult. We present a method to tackle this problem by combining Chinese Sinica Treebank data with unlabelled data to train a better model based on SVM. Experiments with open test data show that our proposed semi-supervised approach can achieve the accuracy of 78.79% in f-measure, enhancing the f-measure by 8.79% over the supervised approach.
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