Assisted Curation: Does Text Mining Really Help?

Beatrice Alex, Claire Grover, Barry Haddow, Mijail Kabadjor, Ewan Klein, Michael Matthews, Stuart Roebuck, Richard Tobin, Xinglong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Although text mining shows considerable promise as a tool for supporting the curation of biomedical text, there is little concrete evidence as to its effectiveness. We report on three experiments measuring the extent to which curation can be speeded up with assistance from Natural Language Processing (NLP), together with subjective feedback from curators on the usability of a curation tool that integrates NLP hypotheses for protein-protein interactions (PPIs). In our curation scenario, we found that a maximum speed-up of 1/3 in curation time can be expected if NLP output is perfectly accurate. The preference of one curator for consistent NLP output and output with high recall needs to be confirmed in a larger study with several curators.
Original languageEnglish
Title of host publicationProceedings of the Pacific Symposium on Biocomputing (Biocomputing 2008)
EditorsRuss B. Altman, A. Keith Dunker, Lawrence Hunter, Tiffany Murray, Teri E. Klein
PublisherSingapore: World Scientific Press
Pages556-567
Number of pages12
Publication statusPublished - 2008

Fingerprint Dive into the research topics of 'Assisted Curation: Does Text Mining Really Help?'. Together they form a unique fingerprint.

Cite this