A new estimate of the n-gram language model
Si Lhoussain Aouragh, Abdellah Yousfi, Saida Laaroussi, Hicham Gueddah, Mohammed Nejja
Abstract
Modeling a natural language aims to build systems that are able to reproduce, correct and more or less predict the structure of a given language; moreover it summarizes the general knowledge related to that language. In this context, we have suggested a new language model which efficiently estimate the n-gram language model. This new model has made it possible to remedy the shortcomings of the n-gram language model. By using an automatic spelling error application, the evaluation of this model has been done, and after the comparison of the results obtained; we see that this new model give quick and precise results. These results show that this new proposed model better models the succession of words within sentences, and can therefore replace the n-gram model in Natural Language Processing (NLP) applications.