期刊名称：The Prague Bulletin of Mathematical Linguistics
印刷版ISSN：0032-6585
电子版ISSN：1804-0462
出版年度：2010
卷号：93
期号：1
页码：137-146
DOI：10.2478/v10108-010-0014-6
语种：English
出版社：Walter de Gruyter GmbH
摘要：This paper describes an open-source implementation of the so-called continuous space language model and its application to statistical machine translation. The underlying idea of this approach is to attack the data sparseness problem by performing the language model probability estimation in a continuous space. The projection of the words and the probability estimation are both performed by a multi-layer neural network. This paper describes the theoretical background of the approach, efficient algorithms to handle the computational complexity, and gives implementation details and reports experimental results on a variety of tasks.
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