Introducing an automated subject classifier

Pru Mitchell; Tine Grimston; Robert Parkes

Introducing an automated subject classifier

Pru Mitchell, Tine Grimston, Robert Parkes

Research output: Contribution to conference › Presentation

Abstract

The library community understands the value of controlled vocabularies in enhancing resource discovery. There is however ongoing tension between that value and the cost of maintaining and applying specialist vocabularies. This paper presents the outcomes of a 2014-15 trial of automated subject indexing at the Australian Council for Educational Research. The integration of a machine learning classification tool has resulted in streamlined workflows and increased use of machine-readable data. Insights were gained into the decisions human indexers make in using a controlled vocabulary, and into the importance of quality abstracts and metadata.

Original language	English
Publication status	Published - 10 Feb 2016
Externally published	Yes

Keywords

automation
classification
curation
discovery
machine learning
machine-readable data
subject indexing
tools

Disciplines

Cataloging and Metadata

Access to Document

Cite this

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N2 - The library community understands the value of controlled vocabularies in enhancing resource discovery. There is however ongoing tension between that value and the cost of maintaining and applying specialist vocabularies. This paper presents the outcomes of a 2014-15 trial of automated subject indexing at the Australian Council for Educational Research. The integration of a machine learning classification tool has resulted in streamlined workflows and increased use of machine-readable data. Insights were gained into the decisions human indexers make in using a controlled vocabulary, and into the importance of quality abstracts and metadata.

AB - The library community understands the value of controlled vocabularies in enhancing resource discovery. There is however ongoing tension between that value and the cost of maintaining and applying specialist vocabularies. This paper presents the outcomes of a 2014-15 trial of automated subject indexing at the Australian Council for Educational Research. The integration of a machine learning classification tool has resulted in streamlined workflows and increased use of machine-readable data. Insights were gained into the decisions human indexers make in using a controlled vocabulary, and into the importance of quality abstracts and metadata.

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KW - classification

KW - curation

KW - discovery

KW - machine learning

KW - machine-readable data

KW - subject indexing

KW - tools

M3 - Presentation

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