Topics in semantic representation
Abstract
Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language-processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.
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Language interpretation is a high-level cognitive task which shows a good correspondence to Bayesian models.
“Language interpretation is a high-level cognitive task which shows a good correspondence to Bayesian models [5,6].”
From Bayesian Brains without Probabilities (2016) · cites this paper as [5]
Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language.
“Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language [1-10]”
From Bayesian Brains without Probabilities (2016) · cites this paper as [5]
Temporal links between words produced in a specific sequence may inform that those words are probably conceptually related, as suggested by semantic association models.
“These temporal links may inform that words produced in a specific temporal sequence are probably conceptually related, as suggested by the semantic association models (McClelland and Rogers, 2003; Griffiths et al., 2007).”
From Graph analysis of verbal fluency test discriminate between patients with Alzheimer’s disease, mild cognitive impairment and normal elderly controls (2014) · cites this paper as Griffiths et al., 2007
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Computational psychotherapy research: scaling up the evaluation of patient–provider interactions
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Self-citationTheoretical and empirical evidence for the impact of inductive biases on cultural evolution
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Self-citationWord meaning in minds and machines.
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Evolution and impact of bias in human and machine learning algorithm interaction
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Checking and bootstrapping lexical norms by means of word similarity indexes
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Psychological foundations of explainability and interpretability in artificial intelligence
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Trait Associations for Hillary Clinton and Donald Trump in News Media: A Computational Analysis
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- Authors
- Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B.
- Affiliations
- University of California, Berkeley (US)
- University of California, Irvine (US)
- Massachusetts Institute of Technology (US)
- Venue
- Psychological Review
- Published
- 2007
- Volume
- 114
- Issue
- 2
- Pages
- 211-244
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W1984251878
- Semantic Scholar
- 509a2ca90a85c62d66a16b37e0de28715dd4e89f
- Cited by
- 1,122
- References
- 119
- Citations by year
- 2026: 27 · 2025: 24 · 2024: 28 · 2023: 46 · 2022: 50 · 2021: 106 · 2020: 68 · 2019: 67 · 2018: 87 · 2017: 66 · 2016: 66 · 2015: 69 · 2014: 73 · 2013: 65 · 2012: 79
- FWCI
- 56.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NTT Communication Sciences Laboratory
- Defense Advanced Research Projects Agency
- Stanford Graduate Fellowship
- National Science Foundation (BCS 0631518)
- Sponsor name not included
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Keywords
- semantic representation, topic modeling, word association, word disambiguation, semantic memory, statistical inference
- MeSH terms
- Bayes Theorem, Humans, Language, Semantics, Speech Perception, Models, Statistical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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