ArticleJournal of cheminformatics2015
The CHEMDNER corpus of chemicals and drugs and its annotation principles.
Article in Journal of cheminformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
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105 citing papers in PubMed.
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- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
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- GRU-SCANET: unleashing the power of GRU-based sinusoidal capture network for precision-driven named entity recognition.Bioinformatics advances · 2025Article
- MilkOligoCorpus: A semantically annotated resource for knowledge extraction on mammalian milk oligosaccharides.PloS one · 2025Article
- MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer.Computational and structural biotechnology journal · 2024Article
- A Database of Stress-Strain Properties Auto-generated from the Scientific Literature using ChemDataExtractor.Scientific data · 2024Article
- EnzChemRED, a rich enzyme chemistry relation extraction dataset.Scientific data · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models.Findings of ACL. ACL · 2024Article
- Application of machine reading comprehension techniques for named entity recognition in materials science.Journal of cheminformatics · 2024Article
- Explanatory argumentation in natural language for correct and incorrect medical diagnoses.Journal of biomedical semantics · 2024Article
- An in-depth evaluation of federated learning on biomedical natural language processing for information extraction.NPJ digital medicine · 2024Article
- BioBBC: a multi-feature model that enhances the detection of biomedical entities.Scientific reports · 2024Article
45 more citing papers are in PubMed but not listed here.
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53 authors.
Funding
Abstract
The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/.
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