Probing the structure and typicality of Chinese emotion words using Neural Networks

Yueh Lin TsaiNational Cheng Kung University, Tainan, Taiwan
Ming Liang WeiNational Cheng Kung University, Tainan, Taiwan
Yu-Chen Chang ChienNational Cheng Kung University, Tainan, Taiwan
Yi-Ling ChungInstitute of Cognitive Science, Tainan, Taiwan
Chao-Ming ChengDepartment of Psychology, Fo Guang University, Taiwan
Shu-Ling ChoDepartment of Clinical Psychology, Fu Jen Catholic University, Taiwan
Hsueh-Chih ChenDepartment of Educational Psychology and Counseling, National Taiwan Normal University, Taiwan
Jon-Fan HuNational Cheng Kung University, Tainan, Taiwan


Previous studies on the structure of emotion concepts often use multidimensional scaling (MDS) or other clustering methods to plot different emotion words upon 2D space or dendrogram, representing each emotion as a point or branch within the structure, and the categories of emotions are determined accordingly. Although there seems to offer the conceptual representations of typical or atypical emotion words categorically, above approaches merely classified an emotion into a single category. Hence, this study is aimed at using Self-Organize Map (SOM) algorithm to explore the semantic structures of Chinese emotion words and reason what indexes would profile typical and atypical emotion words in each semantic category properly. The SOM, an unsupervised artificial neural network, is able to cluster data based on hidden nonlinear relations between information processing nodes. The present study adopts words from a linguistic corpus of Chinese emotion words, and relevant emotional attribute ratings as input for training and testing the SOM model. Results showed that some emotion words were clustered into different categories while repeating the construction of the models. Differences in Quantization Error of attribute distances in the map between emotion words might also reflect their differences of semantic features. According to the findings, theoretical implications on representing the semantic structure of Chinese emotion words are discussed.


Probing the structure and typicality of Chinese emotion words using Neural Networks (20 KB)

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