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dc.contributor.authorAkella, Lakshmi Manohar
dc.date.accessioned2012-08-17T17:02:36Z
dc.date.available2012-08-17T17:02:36Z
dc.date.issued2012-08-17
dc.identifier.urihttp://hdl.handle.net/1912/5337
dc.descriptionTable 1 summarizes the results of the comparison of the precision and recall values of Naïve Bayes and Maximum Entropy classification algorithms with various parameter estimation methods like GIS, IIS, and L-BFGS on the manually annotated American Seashell book including the Decision Tree Learning algorithm implemented in the Natural Language toolkit[http://www.nltk.org/]en_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 algorithmsen_US
dc.format.mimetypetext/plain
dc.relation.ispartofhttp://hdl.handle.net/1912/6236
dc.subjectNaïve Bayes, Maximum Entropy classification, Natural Language toolkiten_US
dc.titleNetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Table 1en_US
dc.typeDataseten_US


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