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.

翻译：命名实体识别（NER）系统在机器翻译、摘要生成和问答等自然语言处理应用中扮演着关键角色。这些系统能够识别涵盖地点、人物和组织等现实世界概念的命名实体。尽管针对英文的NER系统研究广泛，但在低资源语言背景下尚未得到充分关注。本文聚焦于低资源语言的NER任务，并以印度马拉地语为案例展开研究。当前NLP研究的进展主要基于利用BERT等预训练Transformer模型开发NER系统。然而，我们致力于通过融合不同方法的优势，提升基于CNN和LSTM的浅层模型的性能。在Transformer时代，这些传统深度学习模型因其极高的计算效率而仍具重要价值。我们提出了一种混合方法，通过将基于BERT的子词分词器集成到基础CNN/LSTM模型中，实现高效的NER。研究表明，这种用BERT分词器替代传统词级分词器的简单方法，能使基础单层模型的准确率趋近于BERT等深度预训练模型。我们论证了子词分词对NER的重要性，并展示了构建高效NLP系统的研究成果。基于L3Cube-MahaNER数据集，使用MahaBERT、MahaGPT、IndicBERT和mBERT的分词器进行了性能评估。