Proceedings of the Annual Meeting of the Cognitive Science Society

The Linguistic Category Model (LCM) was developed as a manual coding scheme for quantifying abstract mindsetsin human language. Previous attempts to computationally automate the LCM have relied primarily on pre-coded semanticfeatures, which fail to incorporate important contextual information integral to the LCM coding scheme. In this paper, weintroduce Syntax-LCM, a novel method for automating LCM coding using syntax and dependency tree features as predictors ofconstrual level. We compare the accuracy of Syntax-LCM to that of two previously used automated methods: LIWC LCM andBrysbaert concreteness ratings. We find support that the Syntax-LCM approximates the hand-coded LCM with higher accuracycompared to both the Brysbaert and the LIWC LCM. We also provide evidence that the syntactic features accounted for bySyntax-LCM mirror the inclusion criteria in the original coding manual and support theoretical relationships between distanceand abstract thinking.