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    NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Figure 2

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    Scientific names found in American Seashell Book by manual recognition including names with OCR error (63.62Kb)
    Unique NetiNeti results not including names also found using manual markup or Taxon finder with reasons (2.869Kb)
    Scientific names found by NetiNeti in American Seashell Book (51.43Kb)
    Scientific names found by NetiNeti in American Seashell Book (51.50Kb)
    Scientific names founf using TaxonFinder in American Seashell book (35.46Kb)
    Unique TaxonFinder results not including names also found using manual markup or NetiNeti with reasons (3.501Kb)
    Date
    2011-12-30
    Author
    Akella, Lakshmi Manohar  Concept link
    Metadata
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    Citable URI
    https://hdl.handle.net/1912/4966
    Related Material/Data
    https://hdl.handle.net/1912/6236
    DOI
    10.1575/1912/4966
    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.
    Description
    Figure 2 summarizes the results of running the NetiNeti with Naïve Bayes algorithm on the annotated corpus (“American Seashell” book). We also compare our results with those of TaxonFinder.
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    Suggested Citation
    Dataset: Akella, Lakshmi Manohar, "NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Figure 2", 2011-12-30, DOI:10.1575/1912/4966, https://hdl.handle.net/1912/4966
     
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