Using knowledge to improve N-Gram Language Modelling through the MGGI methodology (1996)
| Venue: | In Grammatical Inference: Learning Syntax from Sentences, L.Miclet, C.De La Higuera, Eds. LNAI (1147 |
| Citations: | 9 - 3 self |
BibTeX
@INPROCEEDINGS{Vidal96usingknowledge,
author = {Enrique Vidal and David Llorens},
title = {Using knowledge to improve N-Gram Language Modelling through the MGGI methodology},
booktitle = {In Grammatical Inference: Learning Syntax from Sentences, L.Miclet, C.De La Higuera, Eds. LNAI (1147},
year = {1996},
pages = {179--190},
publisher = {Springer-Verlag}
}
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Abstract
The structural limitations of N-Gram models used for Language Modelling are illustrated through several examples. In most cases of interest, these limitations can be easily overcome using (general) regular or finite-state models, without having to resort to more complex, recursive devices. The problem is how to obtain the required finite-state structures from reasonably small amounts of training (positive) sentences of the considered task. Here this problem is approached through a Grammatical Inference technique known as MGGI. This allows us to easily apply a priory knowledge about the type of syntactic constraints that are relevant to the considered task to significantly improve the performance of N-Grams, using similar or smaller amounts of training data. Speech Recognition experiments are presented with results supporting the interest of the proposed approach.
