A neural probabilistic language model
The Journal of Machine Learning Research
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Discriminative n-gram language modeling
Computer Speech and Language
Using semantic analysis to improve speech recognition performance
Computer Speech and Language
Deep neural network language models
WLM '12 Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT
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This paper presents a new perspective to the language modeling problem by moving the word representations and modeling into the continuous space. In a previous work we introduced Gaussian-Mixture Language Model (GMLM) and presented some initial experiments. Here, we propose Tied-Mixture Language Model (TMLM), which does not have the model parameter estimation problems that GMLM has. TMLM provides a great deal of parameter tying across words, hence achieves robust parameter estimation. As such, TMLM can estimate the probability of any word that has as few as two occurrences in the training data. The speech recognition experiments with the TMLM show improvement over the word trigram model.