Aggregate and mixed-order Markov models for statistical language processing

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We consider the use of language models whose size and accuracy are intermediate between different order n-gram models. Two types of models are studied in particular. Aggregate Markov models are class-based bigram models in which the mapping from words to classes is probabilistic. Mixed-order Markov models combine bigram models whose predictions are conditioned on different words. Both types of models are trained by Expectation-Maximization (EM) algorithms for maximum likelihood estimation. We examine smoothing procedures in which these models are interposed between different order n-grams. This is found to significantly reduce the perplexity of unseen word combinations.
9 pages, 4 PostScript figures, uses psfig.sty and aclap.sty; to appear in the proceedings of EMNLP-2

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Computation and Language

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http://salesiana.dossiersoluciones.com/handle/123456789/151471

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cmp-lg/9706007
 http://arxiv.org/abs/cmp-lg/9706007

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arXiv