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    <abstract>Named Entity Recognition (NER) is a basic NLP task and finds major applications in conversational and search systems. It helps us identify key entities in a sentence used for the downstream application. NER or similar slot filling systems for popular languages have been heavily used in commercial applications. In this work, we focus on Marathi, an Indian language, spoken prominently by the people of Maharashtra state. Marathi is a low resource language and still lacks useful NER resources. We present L3Cube-MahaNER, the first major gold standard named entity recognition dataset in Marathi. We also describe the manual annotation guidelines followed during the process. In the end, we benchmark the dataset on different CNN, LSTM, and Transformer based models like mBERT, XLM-RoBERTa, IndicBERT, MahaBERT, etc. The MahaBERT provides the best performance among all the models. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .</abstract>
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%0 Conference Proceedings
%T L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models
%A Litake, Onkar
%A Sabane, Maithili Ravindra
%A Patil, Parth Sachin
%A Ranade, Aparna Abhijeet
%A Joshi, Raviraj
%Y Jha, Girish Nath
%Y L., Sobha
%Y Bali, Kalika
%Y Ojha, Atul Kr.
%S Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F litake-etal-2022-l3cube
%X Named Entity Recognition (NER) is a basic NLP task and finds major applications in conversational and search systems. It helps us identify key entities in a sentence used for the downstream application. NER or similar slot filling systems for popular languages have been heavily used in commercial applications. In this work, we focus on Marathi, an Indian language, spoken prominently by the people of Maharashtra state. Marathi is a low resource language and still lacks useful NER resources. We present L3Cube-MahaNER, the first major gold standard named entity recognition dataset in Marathi. We also describe the manual annotation guidelines followed during the process. In the end, we benchmark the dataset on different CNN, LSTM, and Transformer based models like mBERT, XLM-RoBERTa, IndicBERT, MahaBERT, etc. The MahaBERT provides the best performance among all the models. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .
%U https://aclanthology.org/2022.wildre-1.6
%P 29-34

Markdown (Informal)

[L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models](https://aclanthology.org/2022.wildre-1.6) (Litake et al., WILDRE 2022)

• L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models (Litake et al., WILDRE 2022)

ACL

• Onkar Litake, Maithili Ravindra Sabane, Parth Sachin Patil, Aparna Abhijeet Ranade, and Raviraj Joshi. 2022. L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models. In Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference, pages 29–34, Marseille, France. European Language Resources Association.