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• W2095817401 abstract "We consider the language identification problem for search engine queries. First, we propose a method to automatically generate a data set, which uses click-through logs of the Yahoo! Search Engine to derive the language of a query indirectly from the language of the documents clicked by the users. Next, we use this data set to train two decision tree classifiers; one that only uses linguistic features and is aimed for textual language identification, and one that additionally uses a non-linguistic feature, and is geared towards the identification of the language intended by the users of the search engine. Our results show that our method produces a highly reliable data set very efficiently, and our decision tree classifier outperforms some of the best methods that have been proposed for the task of written language identification on the domain of search engine queries." @default.
• W2095817401 created "2016-06-24" @default.
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• W2095817401 date "2009-01-01" @default.
• W2095817401 modified "2023-09-24" @default.
• W2095817401 title "Language identification of search engine queries" @default.
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• W2095817401 doi "https://doi.org/10.3115/1690219.1690295" @default.
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