A Preliminary Study On The Construction And Application Of Semantic Features Of Chinese Nouns

Objective:To explore the model of semantic features for Chinese nouns in order to provide quantitative and visual semantic database for clinical speech therapy. Method:The semantic feature entries were collected from a total of 60 stimulating nouns in volunteers by feature nomination. These entries were classified into different feature types according to Semantic Feature Classification Scheme for Chinese. With the R statistical computing environment, the distribution of feature types was visualized and a clustering analysis based on properties was conducted. Results:Firstly, the clusters based on properties were basically in line with the categories based on the experience. Secondly, cognitive processing of different domain have a certain bias. The living domain had significantly more perception properties, and the nonliving domain had significantly more usage properties. Thirdly, the frequency of first rank features were significantly higher than expectation in the taxonomic categories. Conclusion:The model of semantic features for the Chinese nouns by feature nomination can effectively reflect the semantic structures of concepts and help to chose semantic training materials according to quantitative indicators. Objective:To explore the distribution pattern of semantic features among patients with cognitive disorder, verify the rationality of Semantic Feature Classification Scheme for Chinese. Method:The semantic feature entries were collected from a total of 60 stimulating nouns in two patients with apraxia by feature nomination. These entries were classified into different feature types according to Semantic Feature Classification Scheme for Chinese. The distribution of feature types was visualized with the R statistical computing environment. Results:In the domain level, the two patients showed a similar pattern that nonliving domain had significantly more usage properties after treatment. Compared to the normal model, in the second test, the two patients’ distribution manifested that the living domain type bias seemed to disappear, while the nonliving domain showed a type bias approach to normal model. Conclusion:Semantic Feature Classification Scheme for Chinese could evaluate the distribution pattern of semantic feature among patients, provide cues for treatment.