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BioRel: towards large-scale biomedical relation extraction

BACKGROUND: Although biomedical publications and literature are growing rapidly, there still lacks structured knowledge that can be easily processed by computer programs. In order to extract such knowledge from plain text and transform them into structural form, the relation extraction problem becom...

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Detalles Bibliográficos
Autores principales: Xing, Rui, Luo, Jie, Song, Tengwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739482/
https://www.ncbi.nlm.nih.gov/pubmed/33323106
http://dx.doi.org/10.1186/s12859-020-03889-5
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author Xing, Rui
Luo, Jie
Song, Tengwei
author_facet Xing, Rui
Luo, Jie
Song, Tengwei
author_sort Xing, Rui
collection PubMed
description BACKGROUND: Although biomedical publications and literature are growing rapidly, there still lacks structured knowledge that can be easily processed by computer programs. In order to extract such knowledge from plain text and transform them into structural form, the relation extraction problem becomes an important issue. Datasets play a critical role in the development of relation extraction methods. However, existing relation extraction datasets in biomedical domain are mainly human-annotated, whose scales are usually limited due to their labor-intensive and time-consuming nature. RESULTS: We construct BioRel, a large-scale dataset for biomedical relation extraction problem, by using Unified Medical Language System as knowledge base and Medline as corpus. We first identify mentions of entities in sentences of Medline and link them to Unified Medical Language System with Metamap. Then, we assign each sentence a relation label by using distant supervision. Finally, we adapt the state-of-the-art deep learning and statistical machine learning methods as baseline models and conduct comprehensive experiments on the BioRel dataset. CONCLUSIONS: Based on the extensive experimental results, we have shown that BioRel is a suitable large-scale datasets for biomedical relation extraction, which provides both reasonable baseline performance and many remaining challenges for both deep learning and statistical methods.
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spelling pubmed-77394822020-12-17 BioRel: towards large-scale biomedical relation extraction Xing, Rui Luo, Jie Song, Tengwei BMC Bioinformatics Research BACKGROUND: Although biomedical publications and literature are growing rapidly, there still lacks structured knowledge that can be easily processed by computer programs. In order to extract such knowledge from plain text and transform them into structural form, the relation extraction problem becomes an important issue. Datasets play a critical role in the development of relation extraction methods. However, existing relation extraction datasets in biomedical domain are mainly human-annotated, whose scales are usually limited due to their labor-intensive and time-consuming nature. RESULTS: We construct BioRel, a large-scale dataset for biomedical relation extraction problem, by using Unified Medical Language System as knowledge base and Medline as corpus. We first identify mentions of entities in sentences of Medline and link them to Unified Medical Language System with Metamap. Then, we assign each sentence a relation label by using distant supervision. Finally, we adapt the state-of-the-art deep learning and statistical machine learning methods as baseline models and conduct comprehensive experiments on the BioRel dataset. CONCLUSIONS: Based on the extensive experimental results, we have shown that BioRel is a suitable large-scale datasets for biomedical relation extraction, which provides both reasonable baseline performance and many remaining challenges for both deep learning and statistical methods. BioMed Central 2020-12-16 /pmc/articles/PMC7739482/ /pubmed/33323106 http://dx.doi.org/10.1186/s12859-020-03889-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Xing, Rui
Luo, Jie
Song, Tengwei
BioRel: towards large-scale biomedical relation extraction
title BioRel: towards large-scale biomedical relation extraction
title_full BioRel: towards large-scale biomedical relation extraction
title_fullStr BioRel: towards large-scale biomedical relation extraction
title_full_unstemmed BioRel: towards large-scale biomedical relation extraction
title_short BioRel: towards large-scale biomedical relation extraction
title_sort biorel: towards large-scale biomedical relation extraction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7739482/
https://www.ncbi.nlm.nih.gov/pubmed/33323106
http://dx.doi.org/10.1186/s12859-020-03889-5
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