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Indian court legal texts and processes are essential towards the integrity ofthe judicial system and towards maintaining the social and political order ofthe nation. Due to the increase in number of pending court cases, there is anurgent need to develop tools to automate many of the legal processes with theknowledge of artificial intelligence. In this paper, we employ knowledgeextraction techniques, specially the named entity extraction of legal entitieswithin court case judgements. We evaluate several state of the artarchitectures in the realm of sequence labeling using models trained on acurated dataset of legal texts. We observe that a Bi-LSTM model trained onFlair Embeddings achieves the best results, and we also publish the BIOformatted dataset as part of this paper.

FlairNLP at SemEval-2023 Task 6b: Extraction of Legal Named Entities from Legal Texts using Contextual String Embeddings