{"id":151531,"date":"2000-06-01T00:00:00","date_gmt":"2000-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-unified-approach-to-statistical-language-modeling-for-chinese\/"},"modified":"2018-10-16T21:03:55","modified_gmt":"2018-10-17T04:03:55","slug":"a-unified-approach-to-statistical-language-modeling-for-chinese","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-unified-approach-to-statistical-language-modeling-for-chinese\/","title":{"rendered":"A Unified Approach to Statistical Language Modeling for Chinese"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper presents a unified approach to Chinese statistical language modeling (SLM). Applying SLM techniques like trigrams to Chinese is challenging because there is no standard definition of words in Chinese, word boundaries are not marked by spaces, and there is a dearth of training data. Our unified approach automatically and consistently gathers a high-quality training data set from the web, creates a high-quality lexicon, and segments the training data using this lexicon, all using a maximum likelihood principle, which is consistent with the trigram training. We show that each of the methods leads to improvements over standard SLM, and that the combined method yields the best pinyin conversion result reported.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a unified approach to Chinese statistical language modeling (SLM). Applying SLM techniques like trigrams to Chinese is challenging because there is no standard definition of words in Chinese, word boundaries are not marked by spaces, and there is a dearth of training data. Our unified approach automatically and consistently gathers a high-quality 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