Machine Learning for Name Type Classification in Library Metadata
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Description
Poster presented at the 2017 Annual Meeting of the Association for Information Science & Technology. This poster describes a study to investigate automatic type classification using machine learning approaches.
The Digital Projects Unit supports the UNT Libraries with guidance and digital services including imaging, archival storage of electronic files, metadata development, web archiving, and other activities as needed.
Poster presented at the 2017 Annual Meeting of the Association for Information Science & Technology. This poster describes a study to investigate automatic type classification using machine learning approaches.
Physical Description
1 poster : ill. ; 76 x 50 cm.
Notes
Abstract: Library metadata describes resources and objects using standards and s c h e m e s f o r e n c o d i n g a n d representation. Metadata schemes may use different fields for storing and representing name information. Absence of name types is not unusual. It is a known barrier to high-precision information retrieval. Also, name type information can be lost when metadata is mapped between different schemes.
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Phillips, Mark Edward & Chen, Jiangping.Machine Learning for Name Type Classification in Library Metadata,
poster,
October 30, 2017;
(https://digital.library.unt.edu/ark:/67531/metadc1393813/:
accessed April 2, 2023),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT Libraries Digital Projects Unit.
