{"id":282950,"date":"2016-08-24T23:42:48","date_gmt":"2016-08-25T06:42:48","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=282950"},"modified":"2018-10-16T21:25:39","modified_gmt":"2018-10-17T04:25:39","slug":"pos-tagging-hindi-english-code-mixed-text-social-media-machine-learning-experiments-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/pos-tagging-hindi-english-code-mixed-text-social-media-machine-learning-experiments-2\/","title":{"rendered":"POS Tagging of Hindi-English Code Mixed Text from Social Media: Some Machine Learning Experiments"},"content":{"rendered":"<p align=\"LEFT\">We discuss Part-of-Speech(POS) tagging of Hindi-English Code-Mixed(CM) text from social media content. We propose extensions to the existing approaches, we also present a new feature set which addresses the transliteration problem inherent in social media. We achieve an 84% accuracy with the new feature set. We show that the context and joint modelling of language detection and POS tag layers do not help in POS tagging.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We discuss Part-of-Speech(POS) tagging of Hindi-English Code-Mixed(CM) text from social media content. We propose extensions to the existing approaches, we also present a new feature set which addresses the transliteration problem inherent in social media. We achieve an 84% accuracy with the new feature set. 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