BoostER: A Performance Boosting Module for Biomedical Entity Recognition.
Published in BIBM, 2024
Md Abdullah Al Hafiz Khan, Md Shamsuzzaman, Sadid A. Hasan, Mohammad S Sorower, Joey Liu, Vivek Datla, Mladen Milosevic, Gabe Mankovich, Rob van Ommering, Nevenka Dimitrova. In Proceeding of BIBM workshop (AIBH - 2019), San Diego, California, USA.
Abstract: Biomedical Entity Recognition tasks have gained significant importance in the clinical research domain. There has been a lot of prior work on improving entity recognition of biomedical concepts from using rule-based to more contextdependent deep learning-based approaches. However, due to its high domain dependency and distinctive vocabularies, appropriate utilization of contextual knowledge becomes a challenge even for context dependent deep models. To this end, we propose a novel performance-boosting improvement module “BoostER” that can be applied to any existing entity recognition system to boost its performance in terms of precision and F1 scores. The proposed module has been developed on top of a pre-trained BERT model and fine-tuned to give more weights to contextual learning compared to word-specific information. We tested our system with Chemical and Disease Entity Recognition tasks using the BioCreative CDR dataset to demonstrate its effectiveness compared to existing state-of-the-art models.