{"id":151781,"date":"2004-03-01T00:00:00","date_gmt":"2004-03-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/capturing-long-distance-dependency-for-language-modeling-an-empirical-study\/"},"modified":"2018-10-16T21:43:53","modified_gmt":"2018-10-17T04:43:53","slug":"capturing-long-distance-dependency-for-language-modeling-an-empirical-study","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/capturing-long-distance-dependency-for-language-modeling-an-empirical-study\/","title":{"rendered":"Capturing Long Distance Dependency in Language Modeling: An Empirical Study"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper presents an extensive empirical study on two language modeling techniques, linguistically-motivated word kipping and predictive clustering, both of which are used in capturing long distance word dependencies that are beyond the scope of a word trigram model. We compare the techniques to others that were proposed previously for the same purpose. We evaluate the resulting models on the task of Japanese Kana-Kanji conversion. We show that the two techniques, while simple, outperform existing methods studied in this paper, and lead to language models that perform significantly better than a word trigram model. We also investigate how factors such astraining corpus size and genre affect the performance of the models.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents an extensive empirical study on two language modeling techniques, linguistically-motivated word kipping and predictive clustering, both of which are used in capturing long distance word dependencies that are beyond the scope of a word trigram model. We compare the techniques to others that were proposed previously for the same purpose. 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