A bioinformatics analysis of the cell line nomenclature
Bioinformatics
Using Existing Biomedical Resources to Detect and Ground Terms in Biomedical Literature
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Support tools for literature-based information access in molecular biology
Proceedings of the 3rd International Universal Communication Symposium
Exploring Species-Based Strategies for Gene Normalization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Assisted editing in the biomedical domain: motivation and challenges.
Proceedings of the 2013 ACM symposium on Document engineering
Unsupervised corpus distillation for represented indicator measurement on focus species detection
International Journal of Data Mining and Bioinformatics
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In biomedical information extraction (IE), a central problem is the disambiguation of ambiguous names for domain specific entities, such as proteins, genes, etc. One important dimension of ambiguity is the organism to which the entities belong: in order to disambiguate an ambiguous entity name (e.g. a protein), it is often necessary to identify the specific organism to which it refers. In this paper we present an approach to the detection and disambiguation of the focus organism(s), i.e. the organism(s) which are the subject of the research described in scientific papers, which can then be used for the disambiguation of other entities. The results are evaluated against a gold standard derived from IntAct annotations. The evaluation suggests that the results may already be useful within a curation environment and are certainly a baseline for more complex approaches.