L3Cube-MahaSocialNER: A Social Media based Marathi NER Dataset and BERT models

This work introduces the L3Cube-MahaSocialNER dataset, the first and largest social media dataset specifically designed for Named Entity Recognition (NER) in the Marathi language. The dataset comprises 18,000 manually labeled sentences covering eight entity classes, addressing challenges posed by social media data, including non-standard language and informal idioms. Deep learning models, including CNN, LSTM, BiLSTM, and Transformer models, are evaluated on the individual dataset with IOB and non-IOB notations. The results demonstrate the effectiveness of these models in accurately recognizing named entities in Marathi informal text. The L3Cube-MahaSocialNER dataset offers user-centric information extraction and supports real-time applications, providing a valuable resource for public opinion analysis, news, and marketing on social media platforms. We also show that the zero-shot results of the regular NER model are poor on the social NER test set thus highlighting the need for more social NER datasets. The datasets and models are publicly available at https://github.com/l3cube-pune/MarathiNLP

On Significance of Subword tokenization for Low Resource and Efficient Named Entity Recognition: A case study in Marathi

Named Entity Recognition (NER) systems play a vital role in NLP applications such as machine translation, summarization, and question-answering. These systems identify named entities, which encompass real-world concepts like locations, persons, and organizations. Despite extensive research on NER systems for the English language, they have not received adequate attention in the context of low resource languages. In this work, we focus on NER for low-resource language and present our case study in the context of the Indian language Marathi. The advancement of NLP research revolves around the utilization of pre-trained transformer models such as BERT for the development of NER models. However, we focus on improving the performance of shallow models based on CNN, and LSTM by combining the best of both worlds. In the era of transformers, these traditional deep learning models are still relevant because of their high computational efficiency. We propose a hybrid approach for efficient NER by integrating a BERT-based subword tokenizer into vanilla CNN/LSTM models. We show that this simple approach of replacing a traditional word-based tokenizer with a BERT-tokenizer brings the accuracy of vanilla single-layer models closer to that of deep pre-trained models like BERT. We show the importance of using sub-word tokenization for NER and present our study toward building efficient NLP systems. The evaluation is performed on L3Cube-MahaNER dataset using tokenizers from MahaBERT, MahaGPT, IndicBERT, and mBERT.