A Chinese Conceptual Semantic Feature Dataset (CCFD)

Behavior Research Methods (2021)Cite this article

Abstract

Memory and language are important high-level cognitive functions of humans, and the study of conceptual representation of the human brain is a key approach to reveal the principles of cognition. However, this research is often constrained by the availability of stimulus materials. The research on concept representation often needs to be based on a standardized and large-scale database of conceptual semantic features. Although Western scholars have established a variety of English conceptual semantic feature datasets, there is still a lack of a comprehensive Chinese version. In the present study, a Chinese Conceptual semantic Feature Dataset (CCFD) was established with 1,410 concepts including their semantic features and the similarity between concepts. The concepts were grouped into 28 subordinate categories and seven superior categories artificially. The results showed that concepts within the same category were closer to each other, while concepts between categories were farther apart. The CCFD proposed in this study can provide stimulation materials and data support for related research fields. All the data and supplementary materials can be found at https://osf.io/ug5dt/.

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Acknowledgements

This study was supported by the Fundamental Research Funds for the Central Universities (Grant Nos. CUC200A004, CUC18A001, CUC18A003-3 and CUC2019B079), the High-Quality and Cutting-Edge Disciplines Construction Project for Universities in Beijing (Internet Information, Communication University of China), Ministry of Education of the PRC (20YJAZH085), and the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing (Grant No. 2020A09).

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Affiliations

  1. State Key Laboratory of Media Convergence and Communication, Communication University of China, No.1 of Dingfuzhuang East Street, Chaoyang District, Beijing, China

    Yaling Deng, Ye Wang & Lihong Cao

  2. Neuroscience and Intelligent Media Institute, Communication University of China, Beijing, 100024, China

    Yaling Deng, Ye Wang, Chenyang Qiu, Yanzhu Gong & Lihong Cao

  3. TV School, Communication University of China, Beijing, 100024, China

    Zhenchao Hu

  4. Animation and Digital Arts school, Communication University of China, Beijing, 100024, China

    Wenyang Sun

  5. College of Humanities, Communication University of China, Beijing, 100024, China

    Xue Zhao & Wei He

  6. State Key Laboratory of Mathematical Engineering and Advanced Computing, Wuxi, 214125, China

    Lihong Cao

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  1. Yaling Deng
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  2. Ye Wang
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  3. Chenyang Qiu
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  4. Zhenchao Hu
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  6. Yanzhu Gong
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  8. Wei He
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  9. Lihong Cao
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Correspondence to Yaling Deng or Lihong Cao.

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Data availability statement

The datasets generated and analyzed during the current study are available in the supplementary materials (https://osf.io/ug5dt/). If you find any mistake in the dataset, we will appreciate it if you contact us to correct them.

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Deng, Y., Wang, Y., Qiu, C. et al. A Chinese Conceptual Semantic Feature Dataset (CCFD). Behav Res (2021). https://doi.org/10.3758/s13428-020-01525-x

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Keywords

  • concept
  • semantic feature
  • dataset
  • Chinese