Language detection using multinomial naïve bayes algorithm

Vaghasiya Yashvi, Vora Diya, Nehayadav, Rana Manish
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Abstract

In this multilingual world, automatic detection of written or spoken language using Language Identification (LID) technology is a boon in the global communication with people using different languages in different countries. For simplicity and for the purpose of this research, the process of automatically identifying the language(s) from a document is thought of as LID. Lot of ongoing research projects are in the field of Natural Language Processing (NLP) that uses LID as a part of NLP. This field exploits several algorithms evolved in the field of computer science, individually or in combination to achieve accuracy in identifying a language. Among the different approaches adopted in LID,NaïveBayes Classification n-gram text processing seems to be promising.This paper proposes the concept for categorising multiple language texts using Naïve Bayesian algorithms using Machine Learning approaches. Using techniques from existing researches, this paper proposes a way to recognize multilingual documents and calculate the relative proportions of these languages.
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语言检测使用多项naïve贝叶斯算法
在这个多语言的世界里,使用语言识别(LID)技术对书面或口头语言进行自动检测是与不同国家使用不同语言的人进行全球交流的福音。为了简单和本研究的目的,从文档中自动识别语言的过程被认为是LID。许多正在进行的研究项目都是在自然语言处理(NLP)领域,使用LID作为NLP的一部分。该领域利用了计算机科学领域发展起来的几种算法,单独或组合起来实现语言识别的准确性。在LID采用的不同方法中,NaïveBayes分类n-gram文本处理似乎很有前途。本文提出了使用Naïve使用机器学习方法的贝叶斯算法对多语言文本进行分类的概念。本文利用已有研究的技术,提出了一种识别多语言文档并计算这些语言的相对比例的方法。
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