Statistical methods for speech recognition
Statistical methods for speech recognition
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Exploiting syntactic structure for natural language modeling
Exploiting syntactic structure for natural language modeling
Relating probabilistic grammars and automata
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
ICML '05 Proceedings of the 22nd international conference on Machine learning
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
A large scale distributed syntactic, semantic and lexical language model for machine translation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A scalable distributed syntactic, semantic, and lexical language model
Computational Linguistics
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In this paper, we present a directed Markov random field model that integrates trigram models, structural language models (SLM) and probabilistic latent semantic analysis (PLSA) for the purpose of statistical language modeling. The SLM is essentially a generalization of shift-reduce probabilistic push-down automata thus more complex and powerful than probabilistic context free grammars (PCFGs). The added context-sensitiveness due to trigrams and PLSAs and violation of tree structure in the topology of the underlying random field model make the inference and parameter estimation problems plausibly intractable, however the analysis of the behavior of the lexical and semantic enhanced structural language model leads to a generalized inside-outside algorithm and thus to rigorous exact EM type re-estimation of the composite language model parameters.