BioCreative V CDR task corpus: a resource for chemical disease relation extraction

By Jiao Li, Yueping Sun, Robin J. Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J. Mattingly, Thomas C. Wiegers, Zhiyong Lu. 2016

1,061 citations recorded by the source corpus.

Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. Given the nature of both tasks, a test collection is required to contain both disease/chemical annotations and relation annotations in the same set of articles. Despite previous efforts in biomedical corpus construction, none was found to be sufficient for the task. Thus, we developed our own corpus called BC5CDR during the challenge by inviting a team of Medical Subject Headings (MeSH) indexers for disease/chemical entity annotation and Comparative Toxicogenomics Database (CTD) curators for CID relation annotation. To ensure high annotation quality and productivity, detailed annotation guidelines and automatic annotation tools were provided. The resulting BC5CDR corpus consists of 1500 PubMed articles with 4409 annotated chemicals, 5818 diseases and 3116 chemical-disease interactions. Each entity annotation includes both the mention text spans and normalized concept identifiers, using MeSH as the controlled vocabulary. To ensure accuracy, the entities were first captured independently by two annotators followed by a consensus annotation: The average inter-annotator agreement (IAA) scores were 87.49% and 96.05% for the disease and chemicals, respectively, in the test set according to the Jaccard similarity coefficient. Our corpus was successfully used for the BioCreative V challenge tasks and should serve as a valuable resource for the text-mining research community. Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

LinkBERT: Pretraining Language Models with Document Links (2022, neutral): “Named entity recognition BC5-chem (Li et al., 2016) 93.”
Chemical-induced disease relation extraction via convolutional neural network (2017, background): “For instance, according to the task guidelines (15), there should be no relation between C1 and D3 because the concept of death is too general to reflect a CID relation.”
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning (2021, positive): “• Biomedical: We use BC5CDR (Li et al., 2016) and NCBI-disease (Doğan et al.”
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining (2022, methodology): “Relation Extraction GLRE [23], REBEL [24], seq2rel [25] KD-DTI [14], BC5CDR [13], DDI [15] Question Answering QA-Net [26], LUKE [27], BERT [2], PubMedBERT [9], BioELECTRA [28], LinkBERT [29] PubMedQA [16], BioASQ [30, 31] Document Classiﬁcation BERT [2], BlueBERT [8], SciBERT [18], SPECTER [32],…”
PubTator central: automated concept annotation for biomedical full text articles (2019, methodology): “We improved the performance of TaggerOne for chemicals by re-training the model using a combination of the BioCreative V CDR corpus (41) and the CHEMDNER corpus (42).”
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling (2020, background): “Experiments on three document-level relation extraction datasets, DocRED (Yao et al., 2019), CDR (Li et al., 2016), and GDA (Wu et al., 2019b), demonstrate that our ATLOP model signiﬁcantly outperforms the state-of-the-art methods.”
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, background): “Furthermore, reinforcement learning (Feng et al., 2018; Zeng et al., 2018) and adversarial training (Wu et al., 2017; Wang et al., 2018; Han et al., 2018a) have also been adopted in DS.”
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders (2021, positive): “, 2014), BC5CDR-disease (BC5-d), BC5CDRchemical (BC5-c) (Li et al., 2016), AskAPatient (Limsopatham and Collier, 2016) and COMETA (stratified-general split, Basaldella et al.”
Construction of the Literature Graph in Semantic Scholar (2018, methodology): “We used two datasets: i) a biomedical dataset formed by combining MSH (Jimeno-Yepes et al., 2011) and BC5CDR (Li et al., 2016) with UMLS as the reference KB, and ii) a CS dataset we curated using Wikipedia articles about CS concepts with DBpedia as the reference KB. Candidate selection.”
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020, methodology): “Unlike previous work, we focus on document-level relation extraction datasets (Yao et al., 2019; Li et al., 2016a; Wu et al., 2019) from different domains with a large number of relations and entities, which require understanding a document and performing multi-hop reasoning.”
MSˆ2: Multi-Document Summarization of Medical Studies (2021, methodology): “, 2020)), NLP tasks like NER (Nye et al., 2018; Li et al., 2016), relation extraction (Jain et al.”
Self-Alignment Pretraining for Biomedical Entity Representations (2020, background): “We denote the disease and chemical mention sets as “BC5CDR-d” and “BC5CDR-c” respectively.”
DocRED: A Large-Scale Document-Level Relation Extraction Dataset (2019, background): “BC5CDR (Li et al., 2016) is a human-annotated document-level RE dataset consisting of 1 , 500 PubMed documents, which is in the speciﬁc domain of biomedicine considering only the “chemical-induced disease” relation, making it unsuitable for developing general-purpose meth-ods for document-level RE.”