Machine Learning for Name Type Classification in Library Metadata

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This paper describes a study to investigate automatic type classification using machine learning approaches.

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2 p.

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Phillips, Mark Edward & Chen, Jiangping August 9, 2017.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT Libraries Digital Projects Unit to Digital Library, a digital repository hosted by the UNT Libraries. More information about this paper can be viewed below.

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Description

This paper describes a study to investigate automatic type classification using machine learning approaches.

Physical Description

2 p.

Notes

Abstract: This poster reports on the effectiveness of machine
learning approaches to classify common names in library
metadata records using the Library of Congress
Name Authority File. Features extracted from this dataset
were used to train and evaluate classification algorithms
including decision tree, naïve Bayes, random
forest and support vector machine implemented
in Weka, an open-source machine learning platform.
The best performing classifiers were also tested on a
collection of 30,000 names extracted from the UNT
Digital Library This poster presents the feature sets,
their testing results and the information gains of extracted
features. The study demonstrated that machine
learning could effectively classify names as persons
or corporations.

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  • 2017 Annual Meeting of the Association for Information Science & Technology, October 27-November 1, 2017. Washington, DC.

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  • Publication Title: Proceedings of the 80th Annual Meeting of the Association for Information Science & Technology.
  • Page Start: 773
  • Page End: 774

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  • August 9, 2017

Added to The UNT Digital Library

  • Dec. 19, 2018, 12:07 p.m.

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Phillips, Mark Edward & Chen, Jiangping. Machine Learning for Name Type Classification in Library Metadata, paper, August 9, 2017; (digital.library.unt.edu/ark:/67531/metadc1393756/: accessed January 24, 2019), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Digital Projects Unit.