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

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.

## introduction the task of named entity recognition (ner) was first put forward in 1991, after which it gradually became an essential part of natural language processing (nlp). the methods for ner is generally classified into four kinds: rule-based approaches, unsupervised learning approaches, feature-based supervised learning approaches and deep-learning based approaches. the previous methods are mainly rule-based, performing well on small dataset, like the lasie-ii system provided by @xcite . under the rapid development of deep learning since 2013, methods like bilstm crf has been a hot spot in recent years. even now, most of the deep learning methods for ner are based on this framework. but lately, some new research based on the concept of "pre-training" has attracted more and more attention, for example, bert, which stands for bidirectional encoder representations from transformers @xcite . it both pushed the glue score to 80.5 % , which got 7.7 % point absolute improvement and isolation of temperate phages by plaque agar overlay 1. melt soft agar overlay tubes in boiling water and place in the 47c water bath. 2. remove one tube of soft agar from the water bath. 3. add 1.0 ml host culture and either 1.0 or 0.1 ml viral concentrate. 4. mix the contents of the tube well by rolling back and forth between two hands, and immediately empty the tube contents onto an agar plate. 5. sit rt for 5 min. 6. gently spread the top agar over the agar surface by sliding the plate on the bench surface using a circular motion. 7. harden the top agar by not disturbing the plates for 30 min. 8. incubate the plates (top agar side down) overnight to 48h. 9. temperate phage plaques will appear as turbid or cloudy plaques, whereas purely lytic phage will appear as shaply defined, clear plaques. figure 1 : an example wet lab protocol @xcite created a new paradigm of natural language processing task method: using the model pre-trained on a large corpus to complete downstream tasks through fine-tuning. ## task for wnut 2020 shared task-1 @xcite , participants were asked to develop a system that automatically identify entities from the provided lab instructions dataset. the dataset is from wet lab protocols, which usually refer to the experiment instructions in biology or chemistry experiments, involving substances like chemicals, proteins, drugs and other materials. figure 1 shows one representative example of the wet lab protocols. in this shared task, the data was divided into three parts, training data with 370 protocols, development data with 122 protocols and test data with 123 protocols. the data was given in conll format. to sum up, this was a small dataset in type example 1 method extraction 2 modifier high quality genomic 3 reagent dna 4 action dissect 5 amount 1-10mg 6 device flow cytometer 7 time 5 minutes 8 speed 350xg 9 mention ethanol wash 10 location tube 11 numerical 10 times 12 temperature 60c 13 size 0.45m 14 concentration 4% 15 measure-type volume 16 generic-measure @xmath0.0 in order to facilitate narration and comparison, we merged all the files of test provided into one during training and verification, and separated and saved the prediction results of corresponding files by using dictionary when submitting verification. ## conclusion this article introduces our relevant experimental research based on this wnut 2020 shared task-1. by trying bilstm crf, we learn about that the method based on static word vector needs to be trained on a specific dataset, so its transferability is relatively low. the performance of related models in the future, because even the class imported matters. we noted that recently, several papers presented ner studies using convolutional neural network (cnn) @xcite . this may imply that more work combining pre-trained model and cnn will be a new direction for ner studies. additionally, although more and more attention has been focused on deep learning nowadays, the methods based on traditional machine learning still get attention and continue to develop with its interpretability and robustness in specific domain tasks.