Text Classification of Conversational Implicatures Based on Lexical Features

Following the guiding hypothesis in NLP, similar word frequency vectors may have similar implicatures, but some scholars are more inclined conversational implicatures cannot be obtained only through lexical features. To judge which view is more reasonable and explore the reasons for the divergence between them, whether conversational implicatures can be obtained only through lexical features is verified empirically. Main work of this paper includes: First, based on 600 corpora in the annotated dataset, the values of 20 lexical features of each corpus are obtained by automatic calculation. Second, meta-transformer of logistic regression for selecting features is adopted for feature selection and ranking. Third, after determining the features, the text is classified by the binomial logistic regression with the type of implicatures as labels. Fourth, results are tested for significance to identify relationships between variables. Experiments show that there is a statistical dependence between lexical features and conversational implicatures, and the text classification of implicatures can be performed only based on lexical features. In addition, the results of text classification will not be different due to the difference in context utterance or the type of implicature, and the text classification of implicatures only based on “response utterance” is more efficient.