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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.wnut-1.35.pdf