The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Using latent semantic analysis to improve access to textual information
Abstract
This paper describes a new approach for dealing with the vocabulary problem in human-computer interaction. Most approaches to retrieving textual materials depend on a lexical match between words in users' requests and those in or assigned to database objects. Because of the tremendous diversity in the words people use to describe the same object, lexical matching methods are necessarily incomplete and imprecise [5]. The latent semantic indexing approach tries to overcome these problems by automatically organizing text objects into a semantic structure more appropriate for matching user requests. This is done by taking advantage of implicit higher-order structure in the association of terms with text objects. The particular technique used is singular-value decomposition, in which a large term by text-object matrix is decomposed into a set of about 50 to 150 orthogonal factors from which the original matrix can be approximated by linear combination. Terms and objects are represented by 50 to 150 dimensional vectors and matched against user queries in this “semantic” space. Initial tests find this completely automatic method widely applicable and a promising way to improve users' access to many kinds of textual materials, or to objects and services for which textual descriptions are available.
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Cites
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Hypertext: An Introduction and Survey
Conklin (1987) · Computer · doi:10.1109/mc.1987.1663693 · Cited by 2,718
The vocabulary problem in human-system communication
Furnas, G. W., Landauer, T. K., Gomez, L. M., & Dumais, S. T. (1987) · Communications of the ACM · doi:10.1145/32206.32212 · Cited by 1,504
Self-citationAn evaluation of retrieval effectiveness for a full-text document-retrieval system
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Intelligent information-sharing systems
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Automatic keyword classification for information retrieval
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Automatic Document Classification
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Building a user-derived interface
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Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose
Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14401 · Cited by 863
Bilingual Word Representations with Monolingual Quality in Mind
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing · doi:10.3115/v1/w15-1521 · Cited by 315
From Word Embeddings to Pre-Trained Language Models: A State-of-the-Art Walkthrough
Mars, M. (2022) · Applied Sciences · doi:10.3390/app12178805 · Cited by 91
Topic Detection: Identifying Relevant Topics in Tourism Reviews
Menner, T., Höpken, W., Fuchs, M., & Lexhagen, M. (2016) · Information and Communication Technologies in Tourism 2016 · doi:10.1007/978-3-319-28231-2_30 · Cited by 35
Metadata
- Authors
- Dumais, S. T., Furnas, G. W., Landauer, T. K., Deerwester, S., & Harshman, R.
- Venue
- Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '88
- Publisher
- ACM Press
- Published
- 1988
- Pages
- 281-285
- OpenAlex
- W2091273188
- Cited by
- 616
- References
- 11
- FWCI
- 13.74
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Dumais, S. T., Furnas, G. W., Landauer, T. K., Deerwester, S., & Harshman, R. (1988). Using latent semantic analysis to improve access to textual information. Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '88. https://doi.org/10.1145/57167.57214
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
