BENNERD: A Neural Named Entity Linking System for COVID-19

Abstract

We present a biomedical entity linking (EL) system BENNERD that detects named entities in text and links them to the unified medical language system (UMLS) knowledge base (KB) entries to facilitate the corona virus disease 2019 (COVID-19) research. BENNERD mainly covers biomedical domain, especially new entity types (e.g., coronavirus, viral proteins, immune responses) by addressing CORD-NER dataset. It includes several NLP tools to process biomedical texts including tokenization, flat and nested entity recognition, and candidate generation and ranking for EL that have been pre-trained using the CORD-NER corpus. To the best of our knowledge, this is the first attempt that addresses NER and EL on COVID-19-related entities, such as COVID-19 virus, potential vaccines, and spreading mechanism, that may benefit research on COVID-19. We release an online system to enable real-time entity annotation with linking for end users. We also release the manually annotated test set and CORD-NERD dataset for leveraging EL task. The BENNERD system is available at https://aistairc.github.io/BENNERD/.