CoChat
Sign Up
Cited by 414Neurobiology of Language and Bilingualismlinguistics and terminology studiesNatural Language Processing Techniques

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 access
Hoffman, Paul, Lambon Ralph, Matthew A., Rogers, Timothy T.·2013·Behavior Research Methods
DOI 10.3758/s13428-012-0278-x
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3758/s13428-012-0278-x

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 50 references

Research works printed but not linked: 6

  1. British National Corpus Consortium. (2007). British National Corpus version 3 (BNC XMLth ed.). Oxford, U.K.: Oxford University Computing Services.
  2. Cruse, D. A. (1986). Lexical semantics. Cambridge, U.K.: Cambridge University Press.
  3. Head, H. (1926). Aphasia and kindred disorders. Cambridge, U.K.: Cambridge University Press.
  4. Morton, J. (1979). Word recognition. In J. Morton & J. C. Marshall (Eds.), Psycholinguistics 2: Structure and processes (pp. 108–156). Cambridge, MA: MIT Press.
  5. Saffran, E. M., Bogyo, L., Schwartz, M. F., & Marin, O. S. M. (1980). Does deep dyslexia reflect right hemisphere reading? In M. Coltheart, J. C. Marshall, & K. E. Patterson (Eds.), Deep dyslexia (pp. 361–406). London, U.K.: Routledge & Kegan Paul.
  6. Zeno, S., Ivens, S., Millard, R., & Duvvuri, R. (1995). The educator’s word frequency guide. Brewer, NY: Touchstone.

Cited by

Sorted by citations

Related

See all

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
ORCID
Venue
Behavior Research Methods
Publisher
Springer Science and Business Media LLC
Published
2012-12-13
Volume
45
Issue
3
Pages
718-730
ISSN
  • 1554-351X (ISSN-L)
  • 1554-3528
DOI
10.3758/s13428-012-0278-x
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
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 ›

Are you an author, or do you see a problem with this page? Request a review