# Domain specific BERT representation for Named Entity Recognition of lab protocol

arXiv: https://arxiv.org/abs/2012.11145 · HTML: https://mlsanity.com/paper/2012.11145/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models](https://mlsanity.com/paper/2103.06057/) (2021)

## Abstract (arXiv)

Supervised models trained to predict properties from representations have been achieving high accuracy on a variety of tasks. For instance, the BERT family seems to work exceptionally well on the downstream task from NER tagging to the range of other linguistic tasks. But the vocabulary used in the medical field contains a lot of different tokens used only in the medical industry such as the name of different diseases, devices, organisms, medicines, etc. that makes it difficult for traditional BERT model to create contextualized embedding. In this paper, we are going to illustrate the System for Named Entity Tagging based on Bio-Bert. Experimental results show that our model gives substantial improvements over the baseline and stood the fourth runner up in terms of F1 score, and first runner up in terms of Recall with just 2.21 F1 score behind the best one.