Curating Stopwords in Marathi: A TF-IDF Approach for Improved Text Analysis and Information Retrieval

TL;DR

This paper develops a curated stopword list for Marathi using TF-IDF and human evaluation, improving text classification in a low-resource language and providing a methodology for similar languages.

Contribution

It introduces a novel TF-IDF based method combined with human evaluation for stopword curation in Marathi, a low-resource language.

Findings

Stopword removal improves Marathi text classification accuracy.

A curated list of 400 Marathi stopwords is created and publicly released.

The methodology can be adapted for other low-resource languages.

Abstract

Stopwords are commonly used words in a language that are often considered to be of little value in determining the meaning or significance of a document. These words occur frequently in most texts and don't provide much useful information for tasks like sentiment analysis and text classification. English, which is a high-resource language, takes advantage of the availability of stopwords, whereas low-resource Indian languages like Marathi are very limited, standardized, and can be used in available packages, but the number of available words in those packages is low. Our work targets the curation of stopwords in the Marathi language using the MahaCorpus, with 24.8 million sentences. We make use of the TF-IDF approach coupled with human evaluation to curate a strong stopword list of 400 words. We apply the stop word removal to the text classification task and show its efficacy. The work also presents a simple recipe for stopword curation in a low-resource language. The stopwords are integrated into the mahaNLP library and publicly available on https://github.com/l3cube-pune/MarathiNLP .