{"id":151565,"date":"2001-07-01T00:00:00","date_gmt":"2001-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/towards-a-simple-and-accurate-statistical-approach-to-learning-translation-relationships-among-words\/"},"modified":"2018-10-16T21:10:09","modified_gmt":"2018-10-17T04:10:09","slug":"towards-a-simple-and-accurate-statistical-approach-to-learning-translation-relationships-among-words","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/towards-a-simple-and-accurate-statistical-approach-to-learning-translation-relationships-among-words\/","title":{"rendered":"Towards a Simple and Accurate Statistical Approach to Learning Translation Relationships among Words"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We report on a project to derive word translation relationships automatically from parallel corpora. Our effort is distinguished by the use of simpler, faster models than those used in previous high-accuracy approaches. Our methods achieve accuracy on single-word translations that seems comparable to any work previously reported, up to nearly 60% coverage of word types, and they perform particularly well on a class of multi-word compounds of special interest to our translation effort.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We report on a project to derive word translation relationships automatically from parallel corpora. Our effort is distinguished by the use of simpler, faster models than those used in previous high-accuracy approaches. 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