{"title":"Detection and correction of real-word spelling errors in Persian language","authors":"Heshaam Faili","doi":"10.1109/NLPKE.2010.5587806","DOIUrl":null,"url":null,"abstract":"Several statistical methods have already been proposed to detect and correct the real-word errors of a context. However, to the best of our knowledge, none of them has been applied on Persian language yet. In this paper, a statistical method based on mutual information of Persian words to deal with context sensitive spelling errors is presented. Different experiments show the accuracy of correction method on a test data which only contains one real-word error in each sentence to be about 80.5% and 87% with respect to precision and recall metrics.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NLPKE.2010.5587806","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
Several statistical methods have already been proposed to detect and correct the real-word errors of a context. However, to the best of our knowledge, none of them has been applied on Persian language yet. In this paper, a statistical method based on mutual information of Persian words to deal with context sensitive spelling errors is presented. Different experiments show the accuracy of correction method on a test data which only contains one real-word error in each sentence to be about 80.5% and 87% with respect to precision and recall metrics.