Abstract | ||
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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/. |
Year | DOI | Venue |
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2020 | 10.18653/V1/2020.EMNLP-DEMOS.24 | EMNLP |
DocType | Volume | Citations |
Conference | 2020.emnlp-demos | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Mohammad Golam Sohrab | 1 | 30 | 3.94 |
Khoa Duong | 2 | 0 | 0.34 |
Makoto Miwa | 3 | 746 | 44.93 |
Goran Topić | 4 | 0 | 0.34 |
Ikeda Masami | 5 | 0 | 0.34 |
Hiroya Takamura | 6 | 529 | 64.23 |