The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII

Rezarta Islamaj, Po Ting Lai, Chih Hsuan Wei, Ling Luo, Tiago Almeida, Richard A.A. Jonker, Sofia I.R. Conceição, Diana F. Sousa, Cong Phuoc Phan, Jung Hsien Chiang, Jiru Li, Dinghao Pan, Wilailack Meesawad, Richard Tzong Han Tsai, M. Janina Sarol, Gibong Hong, Airat Valiev, Elena Tutubalina, Shao Man Lee, Yi Yu HsuMingjie Li, Karin Verspoor, Zhiyong Lu

研究成果: 雜誌貢獻期刊論文同行評審

3 引文 斯高帕斯(Scopus)

摘要

The BioRED track at BioCreative VIII calls for a community effort to identify, semantically categorize, and highlight the novelty factor of the relationships between biomedical entities in unstructured text. Relation extraction is crucial for many biomedical natural language processing (NLP) applications, from drug discovery to custom medical solutions. The BioRED track simulates a real-world application of biomedical relationship extraction, and as such, considers multiple biomedical entity types, normalized to their specific corresponding database identifiers, as well as defines relationships between them in the documents. The challenge consisted of two subtasks: (i) in Subtask 1, participants were given the article text and human expert annotated entities, and were asked to extract the relation pairs, identify their semantic type and the novelty factor, and (ii) in Subtask 2, participants were given only the article text, and were asked to build an end-to-end system that could identify and categorize the relationships and their novelty. We received a total of 94 submissions from 14 teams worldwide. The highest F-score performances achieved for the Subtask 1 were: 77.17% for relation pair identification, 58.95% for relation type identification, 59.22% for novelty identification, and 44.55% when evaluating all of the above aspects of the comprehensive relation extraction. The highest F-score performances achieved for the Subtask 2 were: 55.84% for relation pair, 43.03% for relation type, 42.74% for novelty, and 32.75% for comprehensive relation extraction. The entire BioRED track dataset and other challenge materials are available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC8-BioRED-track/and https://codalab.lisn.upsaclay.fr/competitions/13377 and https://codalab.lisn.upsaclay.fr/competitions/13378.

原文???core.languages.en_GB???
文章編號baae069
期刊Database : the journal of biological databases and curation
2024
DOIs
出版狀態已出版 - 2024

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