Comparison of natural language processing tools for automatic gene ontology annotation of scientific literature

Abstract

Manual curation of scientific literature for ontologybased 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.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27028v1 | CC BY 4.0 Open Access | rec

Data mining powered by the gene ontology

“…The large majority of text mining approaches for recognizing ontology concepts from text either rely on lexical and syntactic analysis of text in addition to machine learning (Cui et al, ; Jonquet et al, ; Manda, Beasley, & Mohanty, ; Mungall et al, ). Beasley and Manda () recently conducted a comparison of a number of text mining tools at annotating biological literature with GO terms.…”