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dc.contributor.authorAkella, Lakshmi Manohar  Concept link
dc.date.accessioned2012-08-17T17:03:26Z
dc.date.available2012-08-17T17:03:26Z
dc.date.issued2012-08-17
dc.identifier.urihttps://hdl.handle.net/1912/5338
dc.descriptionThe results of running NetiNeti with Naïve Bayes algorithm for classification on 136 PMC full text articlesen_US
dc.description.abstractA scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org we present the comparison results of various machine learning algorithms on our annotated corpus. Naïve Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms.en_US
dc.format.mimetypetext/plain
dc.relation.ispartofhttps://hdl.handle.net/1912/6236
dc.titleNetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Figure 3en_US
dc.typeDataseten_US


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