{"title":"双语语料库汉藏多词对等对的自动习得","authors":"Minghua Nuo, Huidan Liu, Long-Long Ma, Jian Wu, Zhiming Ding","doi":"10.1109/IALP.2011.33","DOIUrl":null,"url":null,"abstract":"This paper aims to construct Chinese-Tibetan multi-word equivalent pair dictionary for Chinese-Tibetan computer-aided translation system. Since Tibetan is a morphologically rich language, we propose two-phase framework to automatically extract multi-word equivalent pairs. First, extract Chinese Multi-word Units (MWUs). In this phase, we propose CBEM model to partition a Chinese sentence into MWUs using two measures of collocation and binding degree. Second, get Tibetan translations of the extracted Chinese MWUs. In the second phase, we propose TSIM model to focus on extracting 1-to-n bilingual MWUs. Preliminary experimental results show that the mixed method combining CBEM model with TSIM model is effective.","PeriodicalId":297167,"journal":{"name":"2011 International Conference on Asian Language Processing","volume":"51 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Automatic Acquisition of Chinese-Tibetan Multi-word Equivalent Pair from Bilingual Corpora\",\"authors\":\"Minghua Nuo, Huidan Liu, Long-Long Ma, Jian Wu, Zhiming Ding\",\"doi\":\"10.1109/IALP.2011.33\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to construct Chinese-Tibetan multi-word equivalent pair dictionary for Chinese-Tibetan computer-aided translation system. Since Tibetan is a morphologically rich language, we propose two-phase framework to automatically extract multi-word equivalent pairs. First, extract Chinese Multi-word Units (MWUs). In this phase, we propose CBEM model to partition a Chinese sentence into MWUs using two measures of collocation and binding degree. Second, get Tibetan translations of the extracted Chinese MWUs. In the second phase, we propose TSIM model to focus on extracting 1-to-n bilingual MWUs. Preliminary experimental results show that the mixed method combining CBEM model with TSIM model is effective.\",\"PeriodicalId\":297167,\"journal\":{\"name\":\"2011 International Conference on Asian Language Processing\",\"volume\":\"51 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Asian Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IALP.2011.33\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Asian Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2011.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Acquisition of Chinese-Tibetan Multi-word Equivalent Pair from Bilingual Corpora
This paper aims to construct Chinese-Tibetan multi-word equivalent pair dictionary for Chinese-Tibetan computer-aided translation system. Since Tibetan is a morphologically rich language, we propose two-phase framework to automatically extract multi-word equivalent pairs. First, extract Chinese Multi-word Units (MWUs). In this phase, we propose CBEM model to partition a Chinese sentence into MWUs using two measures of collocation and binding degree. Second, get Tibetan translations of the extracted Chinese MWUs. In the second phase, we propose TSIM model to focus on extracting 1-to-n bilingual MWUs. Preliminary experimental results show that the mixed method combining CBEM model with TSIM model is effective.