Network and Ensemble Enabled Entity Extraction in Informal Text (NEEEEIT) final report. Page: 4 of 92
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Abstract
This SAND report summarizes the activities and outcomes of the Network and Ensemble
Enabled Entity Extraction in Informal Text (NEEEEIT) LDRD project, which addressed im-
proving the accuracy of conditional random fields for named entity recognition through the use
of ensemble methods.
Conditional random fields (CRFs) are powerful, flexible probabilistic graphical models
often used in supervised machine learning prediction tasks associated with sequence data.
Specifically, they are currently the best known option for named entity recognition (NER)
in text. NER is the process of labeling words in sentences with semantic identifiers such as
"person", "date", or "organization".
Ensembles are a powerful statistical inference meta-method that can make most supervised
machine learning methods more accurate, faster, or both. Ensemble methods are normally best
suited to "unstable" classification methods with high variance error. CRFs applied to NER
are very stable classifiers, and as such, would initially seem to be resistant to the benefits of
ensembles.
The NEEEEIT project nonetheless worked out how to generalize ensemble methods to
CRFs, demonstrated that accuracy can indeed be improved by proper use of ensemble tech-
niques, and generated a new CRF code, "pyCrust" and a surrounding application environment,
"NEEEEIT", which implement those improvements.
The summary practical advice that results from this work, then, is:
" When making use of CRFs for label prediction tasks in machine learning, use the pyCrust
CRF base classifier with NEEEEIT's bagging ensemble implementation. (If those codes
are not available, then de-stablize your CRF code via every means available, and generate
the bagged training sets by hand.)
" If you have ample pre-processing computational time, do "forward feature selection" to
find and remove counter-productive feature classes.
" Conversely, if pre-processing time is limited, use NEEEEIT's "edited clone" pyCrust
mechanism, along with a more modest use of bagging, to generate ensembles much
more quickly.4
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Kegelmeyer, W. Philip; Shead, Timothy M. & Dunlavy, Daniel M. Network and Ensemble Enabled Entity Extraction in Informal Text (NEEEEIT) final report., report, September 1, 2013; Albuquerque, New Mexico. (https://digital.library.unt.edu/ark:/67531/metadc864353/m1/4/: accessed April 25, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.