Text classification for cognitive domains: A case using lexical, syntactic and semantic features

Abstract

Various automated classifiers have been implemented to categorise learning-related texts into cognitive domains. However, existing studies have applied limited linguistic features, and most have focused on texts written in English, with little attention given to Chinese. This study has tried to fill the gaps by applying a comprehensive set of features that have rarely been used collectively in previous research, with a focus on Chinese analytical texts. Experiments were conducted for classifier learning and evaluation, where a feature selection procedure significantly improved the classification performance. The results showed that different types of features complemented each other in forming strong collective representations of the original texts, and the discriminant nature of the features can be reasonably explained by language usage phenomena. The proposed approach could potentially be applied to other datasets of analytical writings involving cognitive domains, and the text features explored could be reused and further refined in future studies.