Yu-Ming Hsieh, Su-Chu Lin, Jason J. S. Chang, Keh-Jiann Chen
{"title":"Improving Chinese Parsing with Special-Case Probability Re-estimation","authors":"Yu-Ming Hsieh, Su-Chu Lin, Jason J. S. Chang, Keh-Jiann Chen","doi":"10.1109/IALP.2013.54","DOIUrl":null,"url":null,"abstract":"Syntactic patterns which are hard to be expressed by binary dependent relations need special treatments, since structure evaluations of such constructions are different from general parsing framework. Moreover, these different syntactic patterns (special cases) should be handled with distinct estimated model other than the general one. In this paper, we present a special-case probability re-estimation model (SCM), integrating the general model with an adoptable estimated model in special cases. The SCM model can estimate evaluation scores in specific syntactic constructions more accurately, and is able for adopting different features in different cases. Experiment results show that our proposed model has better performance than the state-of-the-art parser in Chinese.","PeriodicalId":413833,"journal":{"name":"2013 International Conference on Asian Language Processing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Asian Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2013.54","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Syntactic patterns which are hard to be expressed by binary dependent relations need special treatments, since structure evaluations of such constructions are different from general parsing framework. Moreover, these different syntactic patterns (special cases) should be handled with distinct estimated model other than the general one. In this paper, we present a special-case probability re-estimation model (SCM), integrating the general model with an adoptable estimated model in special cases. The SCM model can estimate evaluation scores in specific syntactic constructions more accurately, and is able for adopting different features in different cases. Experiment results show that our proposed model has better performance than the state-of-the-art parser in Chinese.