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A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.
Abstract
How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched. Prologue "How much do we know at any time? Much more, or so I believe, than we know we know!"--Agatha Christie, The Moving Finger A typical American seventh grader knows the meaning of 10-15 words today that she did not know yesterday. She must
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“This work reveals the vast amount of information now embodied in statistical (but deep, not fi rst order) relations between words and the larger contexts (sentences and texts) in which they occur.”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Landauer & Dumais 1997
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“They were able to successfully predict the onset of schizophrenia by discriminating the speech of healthy controls and those with early onset schizophrenia with ~ 80% accuracy.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [17]
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Metadata
- Authors
- Landauer, T. K., & Dumais, S. T.
- Affiliations
- University of Colorado Boulder (US)
- Venue
- Psychological Review
- Published
- 1997-04
- Volume
- 104
- Issue
- 2
- Pages
- 211-240
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W1983578042
- Cited by
- 6,282
- References
- 103
- Citations by year
- 2026: 108 · 2025: 117 · 2024: 158 · 2023: 236 · 2022: 301 · 2021: 264 · 2020: 321 · 2019: 312 · 2018: 322 · 2017: 268 · 2016: 312 · 2015: 351 · 2014: 354 · 2013: 323 · 2012: 312
- FWCI
- 62.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Review (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Keywords
- latent semantic analysis, vocabulary acquisition, knowledge representation, inductive learning, co-occurrence data, psycholinguistics, word meaning, dimensionality reduction
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