Concept annotation in the CRAFT corpus

By M. Bada, Miriam Eckert, Donald Evans, Kristin Garcia, Krista Shipley, D. Sitnikov, W. Baumgartner, K. Cohen, Karin M. Verspoor, Judith A. Blake and 1 more. BMC Bioinformatics, 2012

304 citations recorded by the source corpus.

Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bb521bb77f61c9aa61355e8bb745bcff11f1f7ae

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Citations in the corpus

An extensive review of tools for manual annotation of documents (2019, background): “precisely identifying biomedical entities, such as genes, proteins and diseases [7].”
The CHEMDNER corpus of chemicals and drugs and its annotation principles (2015, methodology): “The CRAFT corpus [16] is a corpus of 97 full text biomedical articles that contains several different concept annotation types including a type consisting of chemical concepts from the ChEBI ontology [16].”
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing (2019, methodology): “In particular, we retrain the spaCy NER model on each of the four datasets mentioned earlier (BC5CDR, CRAFT, JNLPBA, BioNLP13CG) as well as ﬁve more datasets in Crichton et al. (2017): AnatEM, BC2GM, BC4CHEMD, Linnaeus, NCBI-Disease.”