Comparison of natural language processing tools for automatic gene ontology annotation of scientific literature
- Published
- Accepted
- Subject Areas
- Bioinformatics, Natural Language and Speech
- Keywords
- text mining, gene ontology annotation, literature curation, NLP tools
- Copyright
- © 2018 Beasley et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Comparison of natural language processing tools for automatic gene ontology annotation of scientific literature. PeerJ Preprints 6:e27028v1 https://doi.org/10.7287/peerj.preprints.27028v1
Abstract
Manual curation of scientific literature for ontology-based knowledge representation has proven infeasible and unscalable to the large and growing volume of scientific literature. Automated annotation solutions that leverage text mining and Natural Language Processing (NLP) have been developed to ameliorate the problem of literature curation. These NLP approaches use parsing, syntactical, and lexical analysis of text to recognize and annotate pieces of text with ontology concepts. Here, we conduct a comparison of four state of the art NLP tools at the task of recognizing Gene Ontology concepts from biomedical literature using the Colorado Richly Annotated Full-Text (CRAFT) corpus as a gold standard reference. We demonstrate the use of semantic similarity metrics to compare NLP tool annotations to the gold standard.
Author Comment
This is a preprint submission to PeerJ Preprints.