BENNERD : A Neural Named Entity Linking System for COVID -19 (2020)

Abstract: We present a biomedical entity linking (EL) system BENNERD that detects named enti- ties 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. BEN- NERD mainly covers biomedical domain, es- pecially new entity types (e.g., coronavirus, vi- ral proteins, immune responses) by address- ing CORD-NER dataset. It includes several NLP tools to process biomedical texts includ- ing tokenization, flat and nested entity recog- nition, and candidate generation and rank- ing for EL that have been pre-trained using the CORD-NER corpus. To the best of our knowledge, this is the first attempt that ad- dresses 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/ .

## introduction in response to the coronavirus disease 2019 for global research community to apply recent advances in natural language processing (nlp), covid-19 open research dataset (cord-19) 1 is an emerging research challenge with a resource of over 181,000 scholarly articles that are related to the infectious disease covid-19 caused by severe acute respiratory syndrome coronavirus 2 (sars-cov-2). to facilitate covid-19 studies, since ner is considered a fundamental step in text mining system, @xcite created cord-ner dataset with comprehensive ne annotations. the annotations are based on distant or weak supervision. the dataset includes 29,500 documents from the cord-19 corpus. the cord-ner dataset gives a shed on ner, but they do not address linking task which is important to address covid-19 research. for example, in the example sentence in figure 1 , the mention sars-cov-2 needs to be disambiguated. since the term sars-cov-2 in this sentence refers to a virus, it should be linked to an entry of a virus in the knowledge base, not to an entry of 'sars-cov-2 vaccination', which corresponds to therapeutic or preventive procedure to prevent a disease. we present a bert-based exhaustive neural named entity recognition and disambiguation (bennerd) system. the system is composed of four models: ner model (sohrab and miwa, 2018) that enumerates all possible spans as potential entity mentions and classifies them into entity types, masked language model bert @xcite , candidate generation model to find a list of candidate entities in the unified medical language system (umls) knowledge base (kb) for entity linking (el) and candidate ranking model to disambiguate the entity for concept indexing. the bennerd system provides a web interface to facilitate the process of text annotation and its disambiguation without any training for end users. in addition, we introduce cord-nerd (covid-19 open research dataset for named entity recognition and disambiguation) dataset an extended version of cord-ner as for leveraging el task. ## system description the main objective of this work is to address recent pandemic of covid-19 research. to facilitate covid-19 studies, we introduce the ben- angiotensin-converting enzyme 2 ( ace2 ) as a sars-cov-2 receptor span semantic type name semantic type name semantic type cui: c0960880 name: angiotensin-converting enzyme 2 semantic type: t116 mention embedding cosine similarity cui: c1422064 name: ace2 gene semantic type: t028 t116 t028 t005 t192 cord-19 corpus simstring labelled cord-19 corpus ner model umls knowledgebase entity linking model bennerd web interface articles (unlabelled) angiotensin-converting enzyme 2 ( ace2 ) as a sars-cov-2 receptor bert mention embedding mention classifier mention encoder bert bert bert candidate generation model e 1 e 2 .... e k candidate generation ranking text candidate generation model ner model text entity linking system semi-supervised learning nerd system that finds nested named entities and links them to a umls knowledge base (kb). ben-nerd mainly comprises two platforms: a web interface and a back-end server. the overall workflow of the bennerd system is illustrated in figure 1 . ## experimental settings in this section, we evaluate our toolkit on cord-ner and cord-nerd datasets. ## related work to facilitate the biomedical text mining research on covid-19, recently a few works have reported to address text mining tasks. @xcite created cord-ner dataset with distant or weak supervision and reported first ner performances on different ner models. motivated by this work, we presented a first web-based toolkit that addresses both ner and el. in addition, we also extend the cord-ner dataset to solve el task. @xcite created evidenceminer system that retrieves sentence-level textual evidence from cord-ner dataset. @xcite developed an nlp pipeline to extract drug and vaccine information about sars-cov-2 and other viruses to help biomedical experts to easily track the latest scientific publications. to the best of our knowledge, this work is our first effort to solve both ner and el models in a pipeline manner. ## conclusion we presented the bennerd system for entity linking, hoping that we can bring insights for the covid-19 studies on making scientific discoveries. to the best of our knowledge, bennerd represents the first web-based workflow of ner and el for nlp research that addresses cord-19 dataset that leads to create cord-nerd dataset to facilitate covid-19 work. the online system is available for meeting real-time extraction for end users. the bennerd system is continually evolving; we will continue to improve the system as well as to implement new functions such as relation extraction to further facilitate covid-19 research. we refer to visit https://aistairc.github.io/bennerd/ to know more about bennerd and cord-nerd.