Aggregate and mixed-order Markov models for statistical language processing
| dc.creator | Saul, Lawrence | |
| dc.creator | Pereira, Fernando | |
| dc.date | 1997-06-09 | |
| dc.date.accessioned | 2026-07-07T09:10:50Z | |
| dc.date.available | 2026-07-07T09:10:50Z | |
| dc.description | 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. | |
| dc.description | 9 pages, 4 PostScript figures, uses psfig.sty and aclap.sty; to appear in the proceedings of EMNLP-2 | |
| dc.identifier | https://arxiv.org/abs/cmp-lg/9706007 | |
| dc.identifier | http://arxiv.org/abs/cmp-lg/9706007 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/151471 | |
| dc.subject | Computation and Language | |
| dc.title | Aggregate and mixed-order Markov models for statistical language processing | |
| dc.type | text |