The DOI matches its Crossref record; 48 of the 50 references it cites were found in a registry or an index.
Semantic diversity: A measure of semantic ambiguity based on variability in the contextual usage of words
Open accessAbstract
Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word's semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials.
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“previous studies have shown that atrophy in this region is associated with increased word frequency in patients with FTD. 35 , 44 , 55 , 56”
From Automatic classification of AD pathology in FTD phenotypes using natural speech (2024) · cites this paper as [35]
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Metadata
- Authors
- Hoffman, P., Lambon Ralph, M. A., & Rogers, T. T.
- Affiliations
- University of Manchester (GB)
- University of Wisconsin–Madison (US)
- Corresponding author
- Hoffman, Paul
- Venue
- Behavior Research Methods
- Published
- 2012-12-13
- Volume
- 45
- Issue
- 3
- Pages
- 718-730
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2106872048
- Cited by
- 414
- References
- 50
- Citations by year
- 2026: 30 · 2025: 37 · 2024: 39 · 2023: 48 · 2022: 61 · 2021: 45 · 2020: 43 · 2019: 27 · 2018: 25 · 2017: 14 · 2016: 22 · 2015: 7 · 2014: 10 · 2013: 2 · 2012: 2
- FWCI
- 3.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Funders
- National Institute for Health and Care Research (NF-SI-0510-10272)
- Medical Research Council (G0501632, MR/J004146/1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Arts and Humanities › Language and Linguistics › linguistics and terminology studies
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- semantic diversity, semantic ambiguity, polysemy, context-dependent word meaning, latent semantic analysis, word frequency, imageability, semantic judgment, semantic deficits
- MeSH terms
- Adult, Aged, Aged, 80 and over, Aphasia, Humans, Judgment, Language, Middle Aged, Models, Psychological, Psycholinguistics, Regression Analysis, Semantics
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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