Class-based n-gram models of natural language
Computational Linguistics
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Cooccurrence smoothing for stochastic language modeling
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
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Data sparseness problem is inherent and severe in language modeling. Smoothing techniques are usually widely used to solve this problem. However, traditional smoothing techniques are all based on statistical hypotheses without concerning about linguistic knowledge. This paper introduces semantic information into smoothing technique and proposes a similarity-based smoothing method which is based on both statistical hypothesis and linguistic hypothesis. An experiential iterative algorithm is presented to optimize system parameters. Experiment results prove that compared with traditional smoothing techniques, our method can greatly improve the performance of language model.