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.
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.
Authors: Ian Donaldson; Joel Martin; Berry de Bruijn; Cheryl Wolting; Vicki Lay; Brigitte Tuekam; Shudong Zhang; Berivan Baskin; Gary D Bader; Katerina Michalickova; Tony Pawson; Christopher W V Hogue Journal: BMC Bioinformatics Date: 2003-03-27 Impact factor: 3.169
Authors: Florian Leitner; Martin Krallinger; Carlos Rodriguez-Penagos; Jörg Hakenberg; Conrad Plake; Cheng-Ju Kuo; Chun-Nan Hsu; Richard Tzong-Han Tsai; Hsi-Chuan Hung; William W Lau; Calvin A Johnson; Rune Saetre; Kazuhiro Yoshida; Yan Hua Chen; Sun Kim; Soo-Yong Shin; Byoung-Tak Zhang; William A Baumgartner; Lawrence Hunter; Barry Haddow; Michael Matthews; Xinglong Wang; Patrick Ruch; Frédéric Ehrler; Arzucan Ozgür; Güneş Erkan; Dragomir R Radev; Michael Krauthammer; ThaiBinh Luong; Robert Hoffmann; Chris Sander; Alfonso Valencia Journal: Genome Biol Date: 2008-09-01 Impact factor: 13.583