RNNLM Toolkit
by Tomas Mikolov, 2010-2012 The Project has been moved to http://rnnlm.org Introduction Neural network based language models are nowdays among the most successful techniques for statistical language modeling. They can be easily applied in wide range of tasks, including automatic speech recognition and machine translation, and provide significant improvements over classic backoff n-gram models. The
FBK | Human Language Technology
The IRST Language Modeling Toolkit features algorithms and data structures suitable to estimate, store, and access very large LMs. Our software has been integrated into a popular open source Statistical Machine Translation decoder called Moses, and is compatible with language models created with other tools, such as the SRILM Tooolkit. We have migrated the IRSTLM website under SourceForge. Please,
N-gram 言語モデルを圧縮するには - やた＠はてな日記
はじめに 今回の記事は，以下の論文に関するものです．他にも紹介記事（ACL2011論文「Faster and Smaller N-Gram Language Models」を読んだ - EchizenBlog-Zwei）があるので，そちらでは特に触れられていない部分を（独断と偏見により）解説しています． http://nlp.cs.berkeley.edu/pubs/Pauls-Klein_2011_LM_paper.pdf Adam Pauls and Dan Klein. Faster and Smaller N-Gram Language Models. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp. 258--267, 2011. 概要 こ
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will we ever really replace the n-gram model? part 1
