Automatic Classification of Bengali Sentences Based on Sense Definitions Present in Bengali Wordnet

Pal, Aditya; Saha, Diganta; Dash, Niladri Sekhar

doi:10.5121/ijctcm.2015.5101

Cited by 2 publications

(1 citation statement)

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“…Despite leading in education and economy, NLP research in Marathi has not received much attention in Maharashtra. In contrast research in Hindi has been much more significant (Arora, 2013;Akhtar et al, 2016;Joshi et al, 2016Joshi et al, , 2019Patra et al, 2015) followed by Bengali (Patra et al, 2018;Al-Amin et al, 2017;Pal et al, 2015;Sarkar and Bhowmick, 2017). Recently, to enable cross-lingual NLP, the focus has shifted to building single multilingual models instead of models for individual language (Pires et al, 2019;Conneau and Lample, 2019).…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Deep Learning models for Marathi Text Classification

Kulkarni,

Mandhane,

Likhitkar

et al. 2021

Preprint

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The Marathi language is one of the prominent languages used in India. It is predominantly spoken by the people of Maharashtra. Over the past decade, the usage of language on online platforms has tremendously increased. However, research on Natural Language Processing (NLP) approaches for Marathi text has not received much attention. Marathi is a morphologically rich language and uses a variant of the Devanagari script in the written form. This works aims to provide a comprehensive overview of available resources and models for Marathi text classification. We evaluate CNN, LSTM, ULMFiT, and BERT based models on two publicly available Marathi text classification datasets and present a comparative analysis. The pre-trained Marathi fast text word embeddings by Facebook and IndicNLP are used in conjunction with word-based models. We show that basic single layer models based on CNN and LSTM coupled with FastText embeddings perform on par with the BERT based models on the available datasets. We hope our paper aids focused research and experiments in the area of Marathi NLP.

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