{"id":424578,"date":"2017-09-12T21:09:50","date_gmt":"2017-09-13T04:09:50","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=424578"},"modified":"2018-10-16T20:15:11","modified_gmt":"2018-10-17T03:15:11","slug":"estimating-code-switching-twitter-novel-generalized-word-level-language-detection-technique","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/estimating-code-switching-twitter-novel-generalized-word-level-language-detection-technique\/","title":{"rendered":"Estimating Code-Switching on Twitter with a Novel Generalized Word-Level Language Detection Technique"},"content":{"rendered":"

Word-level language detection is necessary for analyzing code-switched text, where multiple languages could be mixed within a sentence. Existing models are restricted to code-switching between two specific languages and fail in real-world scenarios as text input rarely has a priori information on the languages used. We present a novel unsupervised word-level language detection technique for code-switched text for an arbitrarily large number of languages, which does not require any manually annotated training data. Our experiments with tweets in seven languages show a 74% relative error reduction in word-level labeling with respect to competitive baselines. We then use this system to conduct a large-scale quantitative analysis of code-switching patterns on Twitter, both global as well as\u00a0 region-specific, with <\/span>58<\/span>M tweets.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Word-level language detection is necessary for analyzing code-switched text, where multiple languages could be mixed within a sentence. Existing models are restricted to code-switching between two specific languages and fail in real-world scenarios as text input rarely has a priori information on the languages used. 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