Assisted curation: does text mining really help?
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.
Alex
Beatrice
B
School of Informatics, University of Edinburgh, EH8 9LW, UK. balex@inf.ed.ac.uk
Grover
Claire
C
Haddow
Barry
B
Kabadjov
Mijail
M
Klein
Ewan
E
Matthews
Michael
M
Roebuck
Stuart
S
Tobin
Richard
R
Wang
Xinglong
X
D001185:Artificial Intelligence; D019295:Computational Biology; D016208:Databases, Factual; D016247:Information Storage and Retrieval; D009323:Natural Language Processing; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data