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arXiv · 2009.12997

Fancy Man Lauches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction

Haoding Meng·Qingcheng Zeng·Xiaoyang Fang·Zhexin Liang
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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 \textbf{Bert case} under different situations such as \emph{transformers} versions, case sensitivity that may don't get enough attention before.

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Haoding Meng, Qingcheng Zeng, Xiaoyang Fang, Zhexin Liang. 2020-09-28. Fancy Man Lauches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction. https://arxiv.org/abs/2009.12997

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