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        <title>Fancy Man Launches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction</title>
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        <namePart type="given">Qingcheng</namePart>
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    <abstract>Automatic or semi-automatic conversion of protocols specifying steps in performing a lab procedure into machine-readable format benefits biological research a lot. These noisy, dense, and domain-specific lab protocols processing draws more and more interests with the development of deep learning. This paper presents our teamwork on WNUT 2020 shared task-1: wet lab entity extract, that we conducted studies in several models, including a BiLSTM CRF model and a Bert case model which can be used to complete wet lab entity extraction. And we mainly discussed the performance differences of Bert case under different situations such as transformers versions, case sensitivity that may don’t get enough attention before.</abstract>
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%0 Conference Proceedings
%T Fancy Man Launches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction
%A Zeng, Qingcheng
%A Fang, Xiaoyang
%A Liang, Zhexin
%A Meng, Haoding
%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 zeng-etal-2020-fancy
%X Automatic or semi-automatic conversion of protocols specifying steps in performing a lab procedure into machine-readable format benefits biological research a lot. These noisy, dense, and domain-specific lab protocols processing draws more and more interests with the development of deep learning. This paper presents our teamwork on WNUT 2020 shared task-1: wet lab entity extract, that we conducted studies in several models, including a BiLSTM CRF model and a Bert case model which can be used to complete wet lab entity extraction. And we mainly discussed the performance differences of Bert case under different situations such as transformers versions, case sensitivity that may don’t get enough attention before.
%R 10.18653/v1/2020.wnut-1.39
%U https://aclanthology.org/2020.wnut-1.39
%U https://doi.org/10.18653/v1/2020.wnut-1.39
%P 299-304

Markdown (Informal)

[Fancy Man Launches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction](https://aclanthology.org/2020.wnut-1.39) (Zeng et al., WNUT 2020)

• Fancy Man Launches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction (Zeng et al., WNUT 2020)

ACL

• Qingcheng Zeng, Xiaoyang Fang, Zhexin Liang, and Haoding Meng. 2020. Fancy Man Launches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 299–304, Online. Association for Computational Linguistics.