NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Table 1


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dc.contributor.author Akella, Lakshmi Manohar
dc.date.accessioned 2012-08-17T17:02:36Z
dc.date.available 2012-08-17T17:02:36Z
dc.date.issued 2012-08-17
dc.identifier.uri http://hdl.handle.net/1912/5337
dc.description Table 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.abstract A 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.mimetype text/plain
dc.relation.ispartof http://hdl.handle.net/1912/6236
dc.subject Naïve Bayes, Maximum Entropy classification, Natural Language toolkit en_US
dc.title NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Table 1 en_US
dc.type Dataset en_US

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