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In this paper, we describe the approach that we employed to address the taskof Entity Recognition over Wet Lab Protocols -- a shared task in EMNLPWNUT-2020 Workshop. Our approach is composed of two phases. In the first phase,we experiment with various contextualised word embeddings (like Flair,BERT-based) and a BiLSTM-CRF model to arrive at the best-performingarchitecture. In the second phase, we create an ensemble composed of elevenBiLSTM-CRF models. The individual models are trained on random train-validationsplits of the complete dataset. Here, we also experiment with different outputmerging schemes, including Majority Voting and Structured Learning Ensembling(SLE). Our final submission achieved a micro F1-score of 0.8175 and 0.7757 forthe partial and exact match of the entity spans, respectively. We were rankedfirst and second, in terms of partial and exact match, respectively.

PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet  Lab Protocols using Structured Learning Ensemble and Contextualised  Embeddings