A new estimate of the n-gram language model

Titre	A new estimate of the n-gram language model
Publication Type	Conference Paper
Year of Publication	2021
Authors	Aouragh, SL, Yousfi, A, Laaroussi, S, Gueddah, H, Nejja, M
Conference Name	Procedia CIRP
Mots-clés	Arabic languages, Build systems, Computational linguistics, Correction of spelling error, Elsevier, General knowledge, Language modeling, Modeling languages, N-distant-max model, N-gram language models, Natural language processing systems, Natural languages, spelling errors
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. © 2021 Elsevier B.V.. All rights reserved.
URL	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112416933&doi=10.1016%2fj.procs.2021.05.111&partnerID=40&md5=b893c0817afa2e87e8bd68f189d60faf
DOI	10.1016/j.procs.2021.05.111