Fancy Man Lauches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction
This work addresses the need for automated processing of lab protocols in biological research, but it is incremental as it builds on existing methods for a specific shared task.
The paper tackled the problem of extracting wet lab entities from noisy, domain-specific lab protocols by evaluating a BERT case model and a BiLSTM-CRF model, achieving performance improvements through analysis of factors like transformer versions and case sensitivity.
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.