BoostER: A Performance Boosting Module for Biomedical Entity Recognition 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 rulebased to more contextdependent deep learningbased 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 performanceboosting improvement module xe2x80x9cBoostERxe2x80x9d 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 pretrained BERT model and finetuned to give more weights to contextual learning compared to wordspecific information. We tested our system with Chemical and Disease Entity Recognition tasks using the BioCreative CDR dataset to demonstrate its effectiveness compared to existing stateoftheart models.

Distantly Supervised Biomedical Named Entity Recognition with Dictionary Expansion Stateoftheart biomedical named entity recognition (BioNER) systems apply supervised machine learning models (i.e., relying on human effort for training data annotation) which are not easy to be generalized to new entity types and datasets. We propose a distantly supervised approach, AutoBioNER, that automatically recognizes biomedical entities from massive corpora with userinput dictionaries. AutoBioNER does not need any human annotated data. It relies on incomplete entity dictionaries to provide seeds for each entity type and performs a novel entity set expansion step for corpuslevel new entity recognition and dictionary completion. The expanded dictionaries are used as distant supervision to train a neural model for BioNER. Experimental results show that AutoBioNER achieves the best performance among the methods that only use dictionaries with no additional human effort on BioNER benchmark datasets. It is also demonstrated that the dictionary expansion step plays an important role in the great performances.