{"id":424689,"date":"2017-09-12T21:30:03","date_gmt":"2017-09-13T04:30:03","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=424689"},"modified":"2018-10-16T20:15:22","modified_gmt":"2018-10-17T03:15:22","slug":"english-may-hindi-enhancing-language-identification-automatic-ranking-likeliness-word-borrowing-social-media","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/english-may-hindi-enhancing-language-identification-automatic-ranking-likeliness-word-borrowing-social-media\/","title":{"rendered":"All that is English may be Hindi: Enhancing Language Identification through Automatic Ranking of Likeliness of Word Borrowing in Social Media"},"content":{"rendered":"<p align=\"LEFT\">In this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman\u2019s correlation values, our methods perform more than two times better <span style=\"font-family: NimbusRomNo9L-Regu;font-size: medium\">(<\/span> <span style=\"font-family: CMR10;font-size: medium\">0<\/span><span style=\"font-family: CMMI10;font-size: medium\">.<\/span><span style=\"font-family: CMR10;font-size: medium\">62<\/span><span style=\"font-family: NimbusRomNo9L-Regu;font-size: medium\">) in predicting the borrowing likeliness <\/span>compared to the best performing <span style=\"font-family: NimbusRomNo9L-Regu;font-size: medium\">baseline (<\/span> <span style=\"font-family: CMR10;font-size: medium\">0<\/span><span style=\"font-family: CMMI10;font-size: medium\">.<\/span><span style=\"font-family: CMR10;font-size: medium\">26<\/span><span style=\"font-family: NimbusRomNo9L-Regu;font-size: medium\">) reported in literature. <\/span>Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts. In 88% of cases the annotators felt that the foreign language tag should be replaced by native language tag, thus indicating a huge scope for improvement of automatic language identification systems.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman\u2019s correlation values, our methods perform more than two times better ( 0.62) in predicting the borrowing likeliness compared to the best performing baseline ( 0.26) [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACL","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"2254-2264","msr_page_range_start":"2254","msr_page_range_end":"2264","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proc of EMNLP 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