L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models
This work addresses a gap in NLP for Marathi speakers, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the lack of named entity recognition resources for Marathi, a low-resource language, by creating the first major gold standard dataset and benchmarking it on various models, with MahaBERT achieving the best performance.
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 .