汉语动词语义特征建模与分析

The modeling and analysis of semantic features for Chinese verbs

目的:通过构建汉语动词语义特征常模,为临床言语治疗提供量化和可视化的语义特征数据库。方法:选择30个日常生活常用动词作为刺激词,采集健康人的语义特征,并进行条目编码,然后根据汉语语义特征分型方案对其进行分类。统计软件采用R软件进行数据可视化和统计检验。结果:(1)动词的语义特征以功能用途类显著。(2)动词首位秩次的语义特征以功能用途类显著。(3)一论元结构动词以内省特征显著。结论:根据汉语语义特征数据建立的模型可以有效反映概念语义结构,有助于根据量化指标提取语义训练素材。

Objective： To explore the model of semantic features for Chinese verbs to provide quantitative and visual semantic database for clinical speech therapy.Method： The semantic feature entries were collected from a total of 30 stimulating verbs 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 analyzed based on properties.Result：（1）Verbs nonliving domain had significantly more usage property.（2）The frequency of first rank features were significantly higher than expectation in the function.（3）Verbs with one argument structure had significantly more introspection property.Conclusion： The model of semantic features for the Chinese verbs by feature nomination can effectively reflect the semantic structures of concepts and help to chose semantic training materials according to quantitative indicators.