ArticleScientific data2023
A large dataset of semantic ratings and its computational extension.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- A topic-based framework for interpretable and sparse semantic representations.Behavior research methods · 2026Article
- AI-generated familiarity estimates are a useful new source of information about word knowledge in Simplified Chinese.Behavior research methods · 2026Article
- A Standardized Semantic Feature Norm for 954 Cantonese Concepts.Scientific data · 2025Article
- SocioLex-CZ: Normative estimates for socio-semantic dimensions of meaning for 2,999 words and 1,000 images.Behavior research methods · 2025Article
- Lexical Representations in the Common and Specific Neural Networks for Visual, Phonological, and Semantic Processing in Chinese Reading.Human brain mapping · 2025Article
- Neural representation of mixed feelings during real-time processing of negative words in pun-humor.Communications biology · 2025Article
- Quantifying the Multidimensionality of Abstract Concepts: An Italian Database.Brain sciences · 2025Article
- Evaluating the competence of large language models in ophthalmology clinical practice: a multi-scenario quantitative study.Frontiers in cell and developmental biology · 2025Article
- A large-scale database of Mandarin Chinese word associations from the Small World of Words Project.Behavior research methods · 2024Article
- Plant disease prescription recommendation based on electronic medical records and sentence embedding retrieval.Plant methods · 2023Article
- A large-scale fMRI dataset for human action recognition.Scientific data · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
Evidence from psychology and cognitive neuroscience indicates that the human brain's semantic system contains several specific subsystems, each representing a particular dimension of semantic information. Word ratings on these different semantic dimensions can help investigate the behavioral and neural impacts of semantic dimensions on language processes and build computational representations of language meaning according to the semantic space of the human cognitive system. Existing semantic rating databases provide ratings for hundreds to thousands of words, which can hardly support a comprehensive semantic analysis of natural texts or speech. This article reports a large database, the Six Semantic Dimension Database (SSDD), which contains subjective ratings for 17,940 commonly used Chinese words on six major semantic dimensions: vision, motor, socialness, emotion, time, and space. Furthermore, using computational models to learn the mapping relations between subjective ratings and word embeddings, we include the estimated semantic ratings for 1,427,992 Chinese and 1,515,633 English words in the SSDD. The SSDD will aid studies on natural language processing, text analysis, and semantic representation in the brain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.