Exploiting Transliterated Words for Finding Similarity in Inter-Language News Articles using Machine Learning

TL;DR

This paper introduces a machine learning approach that uses transliterated words to measure similarity between English and Urdu news articles, addressing challenges in low-resource language NLP tasks.

Contribution

It proposes a novel method combining transliteration and machine learning to improve inter-language news article similarity detection, especially for low-resource languages like Urdu.

Findings

The approach effectively identifies similar articles across English and Urdu.

Transliteration enhances the accuracy of cross-language similarity detection.

The method outperforms existing approaches in low-resource language scenarios.

Abstract

Finding similarities between two inter-language news articles is a challenging problem of Natural Language Processing (NLP). It is difficult to find similar news articles in a different language other than the native language of user, there is a need for a Machine Learning based automatic system to find the similarity between two inter-language news articles. In this article, we propose a Machine Learning model with the combination of English Urdu word transliteration which will show whether the English news article is similar to the Urdu news article or not. The existing approaches to find similarities has a major drawback when the archives contain articles of low-resourced languages like Urdu along with English news article. The existing approaches to find similarities has drawback when the archives contain low-resourced languages like Urdu along with English news articles. We used lexicon to link Urdu and English news articles. As Urdu language processing applications like machine translation, text to speech, etc are unable to handle English text at the same time so this research proposed technique to find similarities in English and Urdu news articles based on transliteration.