Using knowledge to improve N-gram language modelling through the MGGI methodology

@inproceedings{Vidal1996UsingKT,
  title={Using knowledge to improve N-gram language modelling through the MGGI methodology},
  author={Enrique Vidal and David Llorens},
  booktitle={ICGI},
  year={1996}
}
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… CONTINUE READING

Results and Topics from this paper.

Key Quantitative Results

  • The increase in word error-rate of MGGI-0 with respect to the exact (and MGGI-1) model is 17%, while the corresponding degradations for the 4-TS, 3-TS and 2-TS models are 70%, 90% and 102%, respectively.

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