@inproceedings{vaidhya-kaushal-2020-iitkgp,
    title = "{IITKGP} at {W}-{NUT} 2020 Shared Task-1: Domain specific {BERT} representation for Named Entity Recognition of lab protocol",
    author = "Vaidhya, Tejas  and
      Kaushal, Ayush",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wnut-1.34",
    doi = "10.18653/v1/2020.wnut-1.34",
    pages = "268--272",
    abstract = "Supervised models trained to predict properties from representations have been achieving high accuracy on a variety of tasks.For in-stance, the BERT family seems to work exceptionally well on the downstream task from NER tagging to the range of other linguistictasks. 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.",
}