Some aspects of proximity searching in text retrieval systems
Journal of Information Science
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
More accurate tests for the statistical significance of result differences
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Bioinformatics
Graph-based concept identification and disambiguation for enterprise search
Proceedings of the 19th international conference on World wide web
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Hybrid pattern matching for complex ontology term recognition
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Structured information provided by manual annotation of proteins with Gene Ontology concepts represents a high-quality reliable data source for the research community. However, a limited scope of proteins is annotated due to the amount of human resources required to fully annotate each individual gene product from the literature. We introduce a novel method for automatic identification of GO terms in natural language text. The method takes into consideration several features: (1) the evidence for a GO term given by the words occurring in text, (2) the proximity between the words, and (3) the specificity of the GO terms based on their information content. The method has been evaluated on the BioCreAtIvE corpus and has been compared to current state of the art methods. The precision reached 0.34 at a recall of 0.34 for the identified terms at rank 1. In our analysis, we observe that the identification of GO terms in the "cellular component" subbranch of GO is more accurate than for terms from the other two subbranches. This observation is explained by the average number of words forming the terminology over the different subbranches.