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- W1653048375 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." @default.
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- W1653048375 date "1996-01-01" @default.
- W1653048375 modified "2023-09-26" @default.
- W1653048375 title "Using knowledge to improve N-Gram language modelling through the MGGI methodology" @default.
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- W1653048375 doi "https://doi.org/10.1007/bfb0033353" @default.
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