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the claim
Semantic similarity between words is measured using distributional and vector space models
the verdict
SUPPORTED
the evidence backs this
refutedsupported
the weight of evidence
7 sources for · 0 against

Multiple studies demonstrate that distributional models and vector spaces are standard methods for measuring semantic similarity between words and texts.

Evidence for · 7
A Vector Space for Distributional Semantics for Entailment
2016 · cited by 22
The paper uses distributional semantic vector spaces to capture semantic similarity and lexical relations.
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The analysis

The claim states that semantic similarity between words is measured using distributional and vector space models. All retrieved papers that directly investigate this topic rely on word embeddings, vector spaces, or distributional semantics to quantify semantic similarity, providing full support for the claim with no conflicting evidence.

More for · 6
LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Items
2020 · cited by 18
The research utilizes embeddings in a semantic space to test and evaluate conceptual and lexical similarity.
Refining Word Representations by Manifold Learning
2019 · cited by 11
The study refines pre-trained word representations in vector spaces to improve measures of semantic similarity.
Lexical semantics enhanced neural word embeddings
2022 · cited by 10
The work aims to improve vector space models in deriving semantic similarity from neural word embeddings.
Centroid analysis: Inferring concept representations from open-ended word responses.
2026 · cited by 0
The study maps responses onto a semantic vector space to measure concept representations and semantic similarity.
Leveraging large language models and embedding representations for enhanced word similarity computation.
2025 · cited by 0
The framework transforms semantic representations into vector embeddings to measure word similarity accurately.
Learning semantic similarity from sentence pairs using hybrid features centric approach and explainable siamese neural networks.
2026 · cited by 0
The research employs semantic embeddings and vector models to compute sentence-level semantic similarity.
Everything we examined (12)
  1. A Vector Space for Distributional Semantics for Entailmentpeer-reviewedsupports
  2. LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Itemspeer-reviewedsupports
  3. Refining Word Representations by Manifold Learningpeer-reviewedsupports
  4. Lexical semantics enhanced neural word embeddingspeer-reviewedsupports
  5. Looking for Semantic Similarity: What a Vector Space Model of Semantics Can Tell Us About Attention in Real-world Scenespeer-reviewedno side takennot shown: read and judged not to bear on this claim
  6. Zero-shot pseudowords memorability via representational content analysis.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  7. Semantic similarity across languages reflects neurocognitive dimensions shaped by climate.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  8. Centroid analysis: Inferring concept representations from open-ended word responses.peer-reviewedsupports
  9. Improved Arabic query expansion using word embedding.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  10. Leveraging large language models and embedding representations for enhanced word similarity computation.peer-reviewedsupports
  11. Word Sense Disambiguation with Wikipedia Entities: A Survey of Entity Linking Approaches.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  12. Learning semantic similarity from sentence pairs using hybrid features centric approach and explainable siamese neural networks.peer-reviewedsupports
The paper trail · every fact has a biography
first checked05 Aug 2026
judged → SUPPORTED · 8405 Aug 2026
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