@inproceedings{sohrab-etal-2020-bennerd,
title = "{BENNERD}: A Neural Named Entity Linking System for {COVID}-19",
author = "Sohrab, Mohammad Golam and
Duong, Khoa and
Miwa, Makoto and
Topi{\'c}, Goran and
Masami, Ikeda and
Hiroya, Takamura",
editor = "Liu, Qun and
Schlangen, David",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-demos.24",
doi = "10.18653/v1/2020.emnlp-demos.24",
pages = "182--188",
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 \url{https://aistairc.github.io/BENNERD/}.",
}
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<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/.</abstract>
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%0 Conference Proceedings
%T BENNERD: A Neural Named Entity Linking System for COVID-19
%A Sohrab, Mohammad Golam
%A Duong, Khoa
%A Miwa, Makoto
%A Topić, Goran
%A Masami, Ikeda
%A Hiroya, Takamura
%Y Liu, Qun
%Y Schlangen, David
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2020
%8 October
%I Association for Computational Linguistics
%C Online
%F sohrab-etal-2020-bennerd
%X 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/.
%R 10.18653/v1/2020.emnlp-demos.24
%U https://aclanthology.org/2020.emnlp-demos.24
%U https://doi.org/10.18653/v1/2020.emnlp-demos.24
%P 182-188
Markdown (Informal)
[BENNERD: A Neural Named Entity Linking System for COVID-19](https://aclanthology.org/2020.emnlp-demos.24) (Sohrab et al., EMNLP 2020)
ACL
- Mohammad Golam Sohrab, Khoa Duong, Makoto Miwa, Goran Topić, Ikeda Masami, and Takamura Hiroya. 2020. BENNERD: A Neural Named Entity Linking System for COVID-19. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 182–188, Online. Association for Computational Linguistics.