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

Janvijay Singh, Anshul Wadhawan


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.
Anthology ID:
2020.wnut-1.35
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
273–280
Language:
URL:
https://aclanthology.org/2020.wnut-1.35
DOI:
10.18653/v1/2020.wnut-1.35
Bibkey:
Cite (ACL):
Janvijay Singh and Anshul Wadhawan. 2020. PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 273–280, Online. Association for Computational Linguistics.
Cite (Informal):
PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings (Singh & Wadhawan, WNUT 2020)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.wnut-1.35.pdf