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Semantic Software Lab
Concordia University
Montréal, Canada

Text Mining

The OrganismTagger System

Our open source OrganismTagger is a hybrid rule-based/machine-learning system that extracts organism mentions from the biomedical literature, normalizes them to their scientific name, and provides grounding to the NCBI Taxonomy database. Our pipeline provides the flexibility of annotating the species of particular interest to bio-engineers on different corpora, by optionally including detection of common names, acronyms, and strains. The OrganismTagger performance has been evaluated on two manually annotated corpora, OT and Linneaus. On the OT corpus, the OrganismTagger achieves a precision and recall of 95% and 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linneaus-100, the results show a precision and recall of 99% and 97% and grounding with an accuracy of 97.4%. It is described in detail in our publication, Naderi, N., T. Kappler, C. J. O. Baker, and R. Witte, "OrganismTagger: Detection, normalization, and grounding of organism entities in biomedical documents", Bioinformatics, vol. 27, no. 19 Oxford University Press, pp. 2721--2729, August 9, 2011.

Text Mining: Wissensgewinnung aus natürlichsprachigen Dokumenten

Witte, R., and J. Mülle (Eds.), Text Mining: Wissensgewinnung aus natürlichsprachigen Dokumenten, Universität Karlsruhe, Fakultät für Informatik, Institut für Programmstrukturen und Datenorganisation (IPD), 2006.

Mutation Miner

Baker, C. J. O., R. Witte, A. B. Gurpur, and V. Ryzhikov, "Mutation Miner", 5th International Conference of the Canadian Proteomics Initiative (CPI 2005), Toronto, Ontario, Canada, May 13–14, 2005.

Mutation Miner

Baker, C. J. O., and R. Witte, "Mutation Miner", 13th Annual International conference on Intelligent Systems for Molecular Biology (ISMB 2005), Detroit, Michigan, USA, June 25–29, 2005.

Ontology Design for Biomedical Text Mining

Baker, C. J. O., and K. - H. Cheung (Eds.), Witte, R., T. Kappler, and C. J. O. Baker, "Ontology Design for Biomedical Text Mining", Semantic Web: Revolutionizing Knowledge Discovery in the Life Sciences, New York, NY, USA : Springer Science+Business Media, pp. 281–313, 2007.
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