论文标题
Wnut 2020年的Fancy Man Lauches Zippo共享任务1：湿实验实体提取的BERT案例模型
Fancy Man Lauches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction
论文作者
论文摘要
协议的自动或半自动转换指定了将实验室程序执行到机器可读格式中的步骤，从而使生物学研究受益匪浅。这些嘈杂，密集和特定领域的实验室协议处理随着深度学习的发展，越来越多的兴趣。本文介绍了我们在WNUT 2020共享任务1：湿实验室实体提取物上的团队合作，我们在几种模型中进行了研究，包括Bilstm CRF模型和BERT案例模型，可用于完成湿法实验室的提取。我们主要讨论了\ textbf {bert case}的性能差异，例如\ emph {transformers}版本，案例敏感性以前可能没有足够的关注。
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.
