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L3Cube-MahaNews: News-Based Short Text and Long Document Classification Datasets in Marathi
Journal
Communications in Computer and Information Science
ISSN
18650929
Date Issued
2024-01-01
Author(s)
Mittal, Saloni
Magdum, Vidula
Hiwarkhedkar, Sharayu
Dhekane, Omkar
Joshi, Raviraj
Abstract
The availability of text or topic classification datasets in the low-resource Marathi language is limited, typically consisting of fewer than 4 target labels, with some achieving nearly perfect accuracy. In this work, we introduce L3Cube-MahaNews, a Marathi text classification corpus that focuses on News headlines and articles. This corpus stands out as the largest supervised Marathi Corpus, containing over 1.05 lakh records classified into a diverse range of 12 categories. To accommodate different document lengths, MahaNews comprises three supervised datasets specifically designed for short text, long documents, and medium paragraphs. The consistent labeling across these datasets facilitates document length-based analysis. We provide detailed data statistics and baseline results on these datasets using state-of-the-art pre-trained BERT models. We conduct a comparative analysis between monolingual and multilingual BERT models, including MahaBERT, IndicBERT, and MuRIL. The monolingual MahaBERT model outperforms all others on every dataset. This work is a part of the L3Cube MahaNLP initiative, more information about it can be found at https://github.com/l3cube-pune/MarathiNLP.
Volume
2046 CCIS
Subjects