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Automatic subject indexing using an associative neural network
Open accessAbstract
The global growth in popularity of the World Wide Web has been enabled in part by the availability of browser based search tools which in turn have led to an increased demand for indexing techniques and technologies.As the amount of globally accessible information in community repositories grows, it is no longer cost-effective for such repositories to be indexed by professional indexers who have been trained to be consistent in subject assignment from controlled vocabulary lists.The era of amateur indexers is thus upon us, and the information infrastructure needs to provide support for such indexing if search of the Net is to produce useful results.In this paper, we propose the ConceptAssigner, an automatic subject indexing system based on a variant of the Hopfield network [ 131.In the application discussed herein, a collection of documents is used to automatically create a subset of a thesaurus termed a Concept Space [4J.To automatically index an individual document, concepts extracted from the given document become the input pattern to a Concept Space represented as a Hopfield network.The Hopfield net parallel spreading activation process produces another set of concepts that are strongly related to the concepts of the input document.Such concepts are suitable for use in an interactive indexing environment.A prototype of our automatic subject indexing system has been implemented as part of the Interspace, a semantic indexing and retrieval environment which supports statisticallybased semantic indexing in a persistent object environment.
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“The selected analyser in the framework performs the tasks of tokenisation and normalisation of a text corpus along with stemming and lemmatization.33,34”
From Automated Knowledge Organization AI ML based Subject Indexing System for Libraries (2023) · cites this paper as [33]
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Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
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Cited by
Automated Knowledge Organization AI ML based Subject Indexing System for Libraries
Ahmed, M., Mukhopadhyay, M., & Mukhopadhyay, P. (2023) · DESIDOC Journal of Library & Information Technology · doi:10.14429/djlit.43.01.18619 · Cited by 20
Metadata
- Authors
- Chung, Y.-M., Pottenger, W. M., & Schatz, B. R.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- ORCID
- Schatz, Bruce R.
- Venue
- Proceedings of the third ACM conference on Digital libraries - DL '98
- Publisher
- ACM Press
- Published
- 1998
- Pages
- 59-68
- OpenAlex
- W2096276775
- Cited by
- 29
- References
- 1
- Citations by year
- 2022: 1 · 2018: 1 · 2017: 1 · 2015: 1
- FWCI
- 1.90
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM international conference on Digital libraries (publishedVersion)
- Funders
- National Science Foundation
- National Aeronautics and Space Administration
- University of Arizona
- Defense Advanced Research Projects Agency
- University of Illinois at Urbana-Champaign
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- automatic subject indexing, associative neural networks, Hopfield neural network, conceptual space, thesauri, spreading activation, semantic indexing, document indexing, controlled vocabulary, interspace
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- Chung, Y.-M., Pottenger, W. M., & Schatz, B. R. (1998). Automatic subject indexing using an associative neural network. Proceedings of the third ACM conference on Digital libraries - DL '98. https://doi.org/10.1145/276675.276682
