BACKGROUND: The vast amount of data published in the primary biomedical 
literature represents a challenge for the automated extraction and codification 
of individual data elements. Biological databases that rely solely on manual 
extraction by expert curators are unable to comprehensively annotate the 
information dispersed across the entire biomedical literature. The development 
of efficient tools based on natural language processing (NLP) systems is 
essential for the selection of relevant publications, identification of data 
attributes and partially automated annotation. One of the tasks of the 
Biocreative 2010 Challenge III was devoted to the evaluation of NLP systems 
developed to identify articles for curation and extraction of protein-protein 
interaction (PPI) data.
RESULTS: The Biocreative 2010 competition addressed three tasks: gene 
normalization, article classification and interaction method identification. The 
BioGRID and MINT protein interaction databases both participated in the 
generation of the test publication set for gene normalization, annotated the 
development and test sets for article classification, and curated the test set 
for interaction method classification. These test datasets served as a gold 
standard for the evaluation of data extraction algorithms.
CONCLUSION: The development of efficient tools for extraction of PPI data is a 
necessary step to achieve full curation of the biomedical literature. NLP 
systems can in the first instance facilitate expert curation by refining the 
list of candidate publications that contain PPI data; more ambitiously, NLP 
approaches may be able to directly extract relevant information from full-text 
articles for rapid inspection by expert curators. Close collaboration between 
biological databases and NLP systems developers will continue to facilitate the 
long-term objectives of both disciplines.