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This article is part of the supplement: The BioCreative II - Critical Assessment for Information Extraction in Biology Challenge

Open Access Research

Automating curation using a natural language processing pipeline

Beatrice Alex*, Claire Grover, Barry Haddow, Mijail Kabadjov, Ewan Klein, Michael Matthews, Richard Tobin and Xinglong Wang

Author Affiliations

School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB, UK

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Genome Biology 2008, 9(Suppl 2):S10  doi:10.1186/gb-2008-9-s2-s10

Published: 1 September 2008

Abstract

Background:

The tasks in BioCreative II were designed to approximate some of the laborious work involved in curating biomedical research papers. The approach to these tasks taken by the University of Edinburgh team was to adapt and extend the existing natural language processing (NLP) system that we have developed as part of a commercial curation assistant. Although this paper concentrates on using NLP to assist with curation, the system can be equally employed to extract types of information from the literature that is immediately relevant to biologists in general.

Results:

Our system was among the highest performing on the interaction subtasks, and competitive performance on the gene mention task was achieved with minimal development effort. For the gene normalization task, a string matching technique that can be quickly applied to new domains was shown to perform close to average.

Conclusion:

The technologies being developed were shown to be readily adapted to the BioCreative II tasks. Although high performance may be obtained on individual tasks such as gene mention recognition and normalization, and document classification, tasks in which a number of components must be combined, such as detection and normalization of interacting protein pairs, are still challenging for NLP systems.