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:
A comparison of NetiNeti, TaxonFinder and FAT tool for the
American Seashell Book book (http://www.biodiversitylibrary.org/item/31699) is presented in Table 2. The FAT approach has lower precision and recall
values compared to NetiNeti and TaxonFinder approaches for this corpus. The names
marked up by the FAT tool were compared with the manual mark up. 869 of the
names identified by FAT did not match with the manually marked up set of names.
Most of these unmatched names are species epithets with authorship information. We
- 16 -
further analyzed a random sample of 100 names out of these 869 names and examined
genus information interpreted by the tool in the marked up tags. 32 of the 100
mismatched names have correctly interpreted genus names and the remaining are all
true false positives with incorrect genus tags. We estimated that 278 of these 869 are
correct identifications and the adjusted precision and recall values for the FAT
approach were summarized in Table 2. For many of the true false positives, the FAT
tool tags the species epithet, but does not seem to recognize the genus name
immediately preceding the species name.
