Text-based Language Identifier using Multinomial Naive Bayes Algorithm

被引：0

作者：

Rawat, Sunita ^{[1
]}

Werulkar, Lakshita ^{[1
]}

Jaywant, Sagarika ^{[1
]}

机构：

[1] Shri Ramdeobaba Coll Engn & Management, Dept Comp Sci & Engn, Nagpur, India

来源：

INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING | 2023年 / 14卷 / 01期

关键词：

Language Identification; Natural Language Processing (NLP); Multinomial Na?ve Bayes (MNB); N-Gram algorithm; Term Frequency-Inverse Document Frequency (TF-IDF);

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Language Identification is among the crucial steps in any NLP based application. Text -based documents and webpages are rapidly increasing in the modern Internet. It is simple to locate documents written in different languages from all across the world that are available with just one click. Therefore, a language identifier is absolutely necessary in order to help the user interpret the content. Language identification has so far tended to be more concentrated on European languages and is still rather limited for Indian Traditional Languages. Many researchers have become more interested in the study of language identification for similar languages from popular languages. In this paper, Multinomial Naive Bayes Algorithm is used for detecting languages in Devanagari like Marathi, Sanskrit and Hindi, and three European languages French, Italian and English. An experiment done on datasets of each language has produced satisfactorily accurate results after training and testing the model.

引用

页码：96 / 102

页数：7

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