{"title":"Domain Dependent Word Segmentation Based on Conditional Random Fields","authors":"T. Fukuda, M. Izumi, T. Miura","doi":"10.1109/PACRIM.2007.4313226","DOIUrl":null,"url":null,"abstract":"In this investigation, we propose an experimental approach for word segmentation in Japanese under domain-dependent situation. We apply Conditional Random Fields (CRF) to our issue. CRF learns several probabilistic parameters from training data with specific feature functions dependent on domains. Here we propose how to define domain specific feature functions.","PeriodicalId":395921,"journal":{"name":"2007 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACRIM.2007.4313226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this investigation, we propose an experimental approach for word segmentation in Japanese under domain-dependent situation. We apply Conditional Random Fields (CRF) to our issue. CRF learns several probabilistic parameters from training data with specific feature functions dependent on domains. Here we propose how to define domain specific feature functions.