{"id":155673,"date":"1999-03-01T00:00:00","date_gmt":"1999-03-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/improved-topic-dependent-language-modeling-using-information-retrieval-techniques\/"},"modified":"2018-10-16T20:04:32","modified_gmt":"2018-10-17T03:04:32","slug":"improved-topic-dependent-language-modeling-using-information-retrieval-techniques","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/improved-topic-dependent-language-modeling-using-information-retrieval-techniques\/","title":{"rendered":"Improved Topic-Dependent Language Modeling Using Information Retrieval Techniques"},"content":{"rendered":"<div class=\"asset-content\">\n<p>N-gram language models are frequently used by the speech recognition systems to constrain and guide the search. N-gram models use only the last N-1 words to predict the next word. Typical values of N that are used range from 2-4. N-gram language models thus lack the long-term context information. We show that the predictive power of the N-gram language models can be improved by using long-term context information about the topic of discussion. We use information retrieval techniques to generalize the available context information for topic-dependent language modeling. We demonstrate the effectiveness of this technique by performing experiments on the Wall Street Journal text corpus, which is a relatively difficult task for topic-dependent language modeling since the text is relatively homogeneous. The proposed method can reduce the perplexity of the baseline language model by 37%, indicating the predictive power of the topic-dependent language model.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>N-gram language models are frequently used by the speech recognition systems to constrain and guide the search. N-gram models use only the last N-1 words to predict the next word. Typical values of N that are used range from 2-4. N-gram language models thus lack the long-term context information. We show that the predictive power [&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":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proc. of the Int. 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