Basic color categories in Mandarin Chinese revealed by cluster analysis

Hsieh, Tsuei-Ju Tracy; Kuriki, Ichiro; Chen, I‐Ping; Muto, Yumiko; Tokunaga, Rumi; Shioiri, Satoshi

doi:10.1167/jov.20.12.6

Cited by 9 publications

(16 citation statements)

References 40 publications

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“…Contrary to some other studies (e.g. Sun & Chen, 2018;or Hsieh et al, 2020), cheng 'orange' and zong 'brown' exhibit low dominance values in our sample. One explanation for such discrepancy is that these previous studies used a fixed-choice approach, so that the participants' responses were constrained.…”

Section: Discussioncontrasting

confidence: 99%

“…With regards to 'brown', we noticed that our speakers used different terms for referring to this color: zong, he and kafei. The latter is more common among Taiwanese speakers (Hsieh et al, 2020), with zong and he being more frequent in Mainland China (Gao & Sutrop, 2014). Still, zong is becoming the dominant term over he, which is perceived as outdated color term.…”

Section: Discussionmentioning

confidence: 99%

“…An additional problem is that the names for basic color categories can change from one variety of the language to another. For instance, speakers of Taiwan Mandarin might prefer kafei 'coffee' to zong or he 'brown' (Hsieh et al, 2020), which are commonly used by speakers of Mainland Mandarin (Gao & Sutrop, 2014).…”

Section: Introductionmentioning

confidence: 99%

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A comparison of basic color terms in Mandarin and Spanish

Xu¹,

Zhu²,

Benítez‐Burraco³

2023

Preprint

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Humans can discriminate between hundreds of colors, which are clustered into a smaller number of color categories that can receive distinctive linguistic labels. Being color perception universal, languages differ in the set of basic color terms (BCTs) they have. Despite numerous prior studies, the exact number of BCTs in Mandarin (Sino-Tibetan, China) and Spanish (Indo-European, Spain) is still debated. Also, cross-linguistic differences can be expected with regards to how BCTs are used for referring to color, as well as concerning the naming strategies used for making reference more specific. For clarifying this, we recruited 21 speakers of Mainland Mandarin and 21 speakers of Castilian Spanish and assessed them in two related tasks: the list task, in which participants are asked to write down as many color names as they can recall; and the color naming task, in which they were asked to name color chips from the Munsell color chart that are presented randomly. Our results suggest that Mandarin has 9 BCTs, while Spanish has 12 BCTs. Additionally, we found that, in order to make color reference more exact, speakers of Mandarin use more modified terms and compounds, whereas speakers of Spanish rely more on monomorphemic non-basic terms. Finally, we observed that speakers of Mandarin use some colors (gray, blue and purple) less consistently, although the color space is partitioned similarly by these two languages. Our results reinforce the view that there are universal constraints on color naming, that are compatible with subtle cross-cultural differences in how colors are used.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A comparison of basic color terms in Mandarin and Spanish

Xu¹,

Zhu²,

Benítez‐Burraco³

2023

Preprint

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“…This can avoid over-learning insignificant features in short texts. We adjust formula (5) to formula (7) and (8) to calculate.…”

Section: L1 Normal Form Regularizationmentioning

confidence: 99%

“…This paper uses the K-means algorithm to randomly select K short text vectors from the low-dimensional short text feature vectors obtained by training as the initial cluster centers. According to the distance from the cluster center, we assign each other short text vectors to the nearest cluster [7]. Then we recalculate the mean of each cluster.…”

Section: Short Text Clusteringmentioning

confidence: 99%

Automatic Knowledge Integration Method of English Translation Corpus Based on Kmeans Algorithm

Liang

Bayatti

2023

Applied Mathematics and Nonlinear Sciences

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We propose a feature extraction method based on the Kmeans algorithm based on the text characteristics in the English translation corpus. The article first uses a sparse autoencoder unsupervised learning method to reduce dimensionality. It then uses the Kmeans clustering algorithm for text clustering. The experimental results prove that the text features extracted by the sparse autoencoder based on the Kmeans algorithm can be used for English translation corpus knowledge clustering to achieve automatic integration. And this method can effectively solve the problems of high-dimensional, sparse, and noisy texts in the English translation corpus. The algorithm mentioned in the article can significantly improve the accuracy of the clustering results.

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A comparison of basic color terms in Mandarin and Spanish

Zhu²,

Benítez‐Burraco

2023

Color Research & Application

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Basic perceptual abilities are universal, and most people can distinguish between millions of distinct shades. However, languages differ in the set of basic color terms (BCTs) they have. Despite numerous prior studies, the exact number of BCTs in Mandarin (Sino‐Tibetan) and Spanish (Indo‐European) is still debated. In Mandarin, this number varies from 6 to 11, whereas in Spanish there are about 11 or 12 BCTs. Additionally, differences between these two languages can be expected with regard to the differing usage of BCTs in reference to color, as well as the naming strategies used for specifying references. To clarify this, we recruited a group of Mainland Mandarin speakers and a group of Castilian Spanish speakers and assessed them in two related tasks: the list task, in which participants were asked to write down as many color names as they could recall; and the color‐naming task, in which they were asked to name color chips from the Munsell color chart that were presented randomly. Our results suggest that Mandarin has nine BCTs, while Spanish has 12 BCTs. Furthermore, we found that Mandarin speakers use more modified terms and compounds to achieve precise color reference, whereas Spanish speakers rely more on monomorphemic non‐basic terms. Finally, we observed that Mandarin speakers use some colors (gray, blue, and purple) less consistently; although, the color space is partitioned similarly by these two languages. Our results reinforce the view that there are universal constraints on color naming that are compatible with subtle cross‐cultural differences in how colors are used.

show abstract

Basic color categories in Mandarin Chinese revealed by cluster analysis

Cited by 9 publications

References 40 publications

A comparison of basic color terms in Mandarin and Spanish

A comparison of basic color terms in Mandarin and Spanish

Automatic Knowledge Integration Method of English Translation Corpus Based on Kmeans Algorithm

A comparison of basic color terms in Mandarin and Spanish

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