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.

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1 poster : ill. ; 76 x 50 cm.

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Phillips, Mark Edward & Chen, Jiangping October 30, 2017.

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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.

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.

Source

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

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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  • October 30, 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, poster, October 30, 2017; (digital.library.unt.edu/ark:/67531/metadc1393813/: accessed January 20, 2019), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Digital Projects Unit.