Kannada Named Entity Recognition Using Deep Learning Techniques

Named Entity Recognition (NER) is a natural language processing task concerned with identifying mentions of named entities and classifying them according to a predefined set of categories. Despite the success of NER in domains, where such data is abundant it remains a formidable challenge for low-resource languages such as Kannada. In this paper we discuss the possible ways to approach NER for the Kannada language.
We explore various research directions including rule-based methods statistical machine learning neural networks and transformers based tagging methodologies. We highlight the various challenges in achieving NER for such a language and propose a transformer based contextual tagging framework for labelling sequences.
We propose to use mBERT IndicBERT and XLM-RoBERTa language models pretrained on target and other related Indic language corpora and further fine-tune these models for the NER task. We discuss various aspects for experimentation including data collection labelling data preparation methods data-splits evaluation metrics comparison with other models hyper parameter tuning entity-wise analysis and error analysis.

Kannada, Named Entity Recognition, Natural Language Processing, Low-Resource Languages, Deep Learning, Transformer, BERT, IndicBERT, XLM-RoBERTa

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