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        <title>PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings</title>
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        <namePart type="given">Janvijay</namePart>
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            <title>Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)</title>
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            <namePart type="given">Wei</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings
%A Singh, Janvijay
%A Wadhawan, Anshul
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F singh-wadhawan-2020-publishincovid19
%X 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.
%R 10.18653/v1/2020.wnut-1.35
%U https://aclanthology.org/2020.wnut-1.35
%U https://doi.org/10.18653/v1/2020.wnut-1.35
%P 273-280

Markdown (Informal)

[PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings](https://aclanthology.org/2020.wnut-1.35) (Singh & Wadhawan, WNUT 2020)

• PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings (Singh & Wadhawan, WNUT 2020)

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.