AZuRE, a Scalable System for Automated Term Disambiguation of Gene and Protein Names
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Improving the performance of dictionary-based approaches in protein name recognition
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Term identification in the biomedical literature
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Notions of correctness when evaluating protein name taggers
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
GAPSCORE: finding gene and protein names one word at a time
Bioinformatics
Protein names precisely peeled off free text
Bioinformatics
Gene name ambiguity of eukaryotic nomenclatures
Bioinformatics
Effective adaptation of a Hidden Markov Model-based named entity recognizer for biomedical domain
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
The GENIA corpus: an annotated research abstract corpus in molecular biology domain
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Two learning approaches for protein name extraction
Journal of Biomedical Informatics
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Gene and protein name identification in text requires a dictionary approach to relate synonyms to the same gene or protein, and to link names to external databases. However, existing dictionaries are incomplete. We investigate two complementary methods for automatic generation of a comprehensive dictionary: combination of information from existing gene and protein databases and rule-based generation of spelling variations. Both methods have been reported in literature before, but have hitherto not been combined and evaluated systematically. We combined gene and protein names from several existing databases of four different organisms. The combined dictionaries showed a substantial increase in recall on three different test sets, as compared to any single database. Application of 23 spelling variation rules to the combined dictionaries further increased recall. However, many rules appeared to have no effect and some appear to have a detrimental effect on precision.