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

TL;DR

This paper explores the effectiveness of a Bert case model for wet lab entity extraction, analyzing how different transformer versions and case sensitivity impact performance in the context of biological protocol processing.

Contribution

It introduces a Bert case model for wet lab entity extraction and investigates the effects of transformer versions and case sensitivity on model performance.

Findings

Bert case model outperforms BiLSTM CRF in wet lab entity extraction

Transformer version impacts model accuracy significantly

Case sensitivity influences extraction performance

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.