Bifurcation of Semi-Automated Subject Indexing Services

Semi-automated subject indexing methods use attributes from metadata descriptions as training data. A survey to shape inclusion of metadata attributes that align a machine learning model within a contextual linguistic domain generated the initial genre targets for experimentation. The second part of...

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Bibliographic Details
Published inLibrary resources & technical services Vol. 68; no. 3
Main Author Hahn, Jim
Format Journal Article
LanguageEnglish
Published Chicago American Library Association 01.01.2024
Subjects
Abstracting and indexing services
Artificial intelligence
Automation
Bifurcations
Collaboration
Forecasts and trends
Indexing
Indexing systems
Machine learning
Market trend/market analysis
Mechanization
Metadata
Software
United States
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Summary:Semi-automated subject indexing methods use attributes from metadata descriptions as training data. A survey to shape inclusion of metadata attributes that align a machine learning model within a contextual linguistic domain generated the initial genre targets for experimentation. The second part of this study then tested the genre attributes from the survey. These bifurcations (or branching points) served as the basis for machine learning model development and evaluation. The machine learning models in semi-automated indexing systems are the drivers of the automated subject outputs. The initial results of this multipart experiment indicate that measures of the mean precision and recall (the F1 metric) improved for several--but not all-types of genres that were of interest to knowledge workers.
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ISSN:2159-9610
0024-2527
2159-9610
DOI:10.5860/lrts.68n3.8066