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

• URL: http://arxiv.org/abs/2010.02142v2
• Date: Thu, 15 Oct 2020 08:33:45 GMT
• Title: PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings
• Authors: Janvijay Singh, Anshul Wadhawan
• Abstract summary: We describe the approach that we employed to address the task of Entity Recognition over Wet Lab Protocols. In the first phase, we experiment with various contextualised word embeddings and a BiLSTM-CRF model. In the second phase, we create an ensemble composed of eleven BiLSTM-CRF models. Our final submission achieved a micro F1-score of 0.8175 and 0.7757 for the partial and exact match of the entity spans.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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