{"title":"Pronominal Resolution in Tamil Using Tree CRFs","authors":"R. Ram, S. L. Devi","doi":"10.1109/IALP.2013.59","DOIUrl":null,"url":null,"abstract":"We describe our work on pronominal resolution in Tamil using Tree CRFs. Pronominal resolution is the task of identifying the referent of a pronominal. In this work we have studied third person pronouns in Tamil such as 'avan', 'aval', 'athu', 'avar', he, she, it and they respectively. Tamil is a Dravidian language and it is morphologically rich and highly agglutinative language. Tree CRFs is a machine learning method, in which the data is modeled as a graph with edge weights used for learning. The features for learning are developed by using the morphological features of the language. The work is carried out on tourism domain data from the Web. We have obtained 70.8% precision and 66.5% recall. The results are encouraging.","PeriodicalId":413833,"journal":{"name":"2013 International Conference on Asian Language Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Asian Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2013.59","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
We describe our work on pronominal resolution in Tamil using Tree CRFs. Pronominal resolution is the task of identifying the referent of a pronominal. In this work we have studied third person pronouns in Tamil such as 'avan', 'aval', 'athu', 'avar', he, she, it and they respectively. Tamil is a Dravidian language and it is morphologically rich and highly agglutinative language. Tree CRFs is a machine learning method, in which the data is modeled as a graph with edge weights used for learning. The features for learning are developed by using the morphological features of the language. The work is carried out on tourism domain data from the Web. We have obtained 70.8% precision and 66.5% recall. The results are encouraging.
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