  In this paper, we describe the approach that we employed to address the task
of Entity Recognition over Wet Lab Protocols -- a shared task in EMNLP
WNUT-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-performing
architecture. In the second phase, we create an ensemble composed of eleven
BiLSTM-CRF models. The individual models are trained on random train-validation
splits of the complete dataset. Here, we also experiment with different output
merging schemes, including Majority Voting and Structured Learning Ensembling
(SLE). Our final submission achieved a micro F1-score of 0.8175 and 0.7757 for
the partial and exact match of the entity spans, respectively. We were ranked
first and second, in terms of partial and exact match, respectively.
