Ying Lin, Xiaoman Pan, Aliya Deri, Heng Ji, Kevin Knight
{"title":"利用实体链接和相关语言投射来提高姓名音译","authors":"Ying Lin, Xiaoman Pan, Aliya Deri, Heng Ji, Kevin Knight","doi":"10.18653/v1/W16-2701","DOIUrl":null,"url":null,"abstract":"Traditional name transliteration methods largely ignore source context information and inter-dependency among entities for entity disambiguation. We propose a novel approach to leverage state-of-the-art Entity Linking (EL) techniques to automatically correct name transliteration results, using collective inference from source contexts and additional evidence from knowledge base. Experiments on transliterating names from seven languages to English demonstrate that our approach achieves 2.6% to 15.7% absolute gain over the baseline model, and significantly advances state-of-the-art. When contextual information exists, our approach can achieve further gains (24.2%) by collectively transliterating and disambiguating multiple related entities. We also prove that combining Entity Linking and projecting resources from related languages obtained comparable performance as themethod using the same amount of training pairs in the original languageswithout Entity Linking.1","PeriodicalId":254249,"journal":{"name":"NEWS@ACM","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration\",\"authors\":\"Ying Lin, Xiaoman Pan, Aliya Deri, Heng Ji, Kevin Knight\",\"doi\":\"10.18653/v1/W16-2701\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional name transliteration methods largely ignore source context information and inter-dependency among entities for entity disambiguation. We propose a novel approach to leverage state-of-the-art Entity Linking (EL) techniques to automatically correct name transliteration results, using collective inference from source contexts and additional evidence from knowledge base. Experiments on transliterating names from seven languages to English demonstrate that our approach achieves 2.6% to 15.7% absolute gain over the baseline model, and significantly advances state-of-the-art. When contextual information exists, our approach can achieve further gains (24.2%) by collectively transliterating and disambiguating multiple related entities. We also prove that combining Entity Linking and projecting resources from related languages obtained comparable performance as themethod using the same amount of training pairs in the original languageswithout Entity Linking.1\",\"PeriodicalId\":254249,\"journal\":{\"name\":\"NEWS@ACM\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NEWS@ACM\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/W16-2701\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NEWS@ACM","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/W16-2701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration
Traditional name transliteration methods largely ignore source context information and inter-dependency among entities for entity disambiguation. We propose a novel approach to leverage state-of-the-art Entity Linking (EL) techniques to automatically correct name transliteration results, using collective inference from source contexts and additional evidence from knowledge base. Experiments on transliterating names from seven languages to English demonstrate that our approach achieves 2.6% to 15.7% absolute gain over the baseline model, and significantly advances state-of-the-art. When contextual information exists, our approach can achieve further gains (24.2%) by collectively transliterating and disambiguating multiple related entities. We also prove that combining Entity Linking and projecting resources from related languages obtained comparable performance as themethod using the same amount of training pairs in the original languageswithout Entity Linking.1