Scales or features in verb meaning?: Verb classes as predictors of syntactic behavior

Abstract

Several syntactic properties of verbal heads are accounted for through their semantic properties. Verbal features such as agentivity, volitionality, stativity etc. have been proven a useful tool for predicting several aspects of their syntactic behavior such as passivization, auxiliary selection etc. In the context of the empirical turn in current linguistics, the assumption of discrete features is questioned by studies based on corpora or speakers’ intuitions showing that the diagnostics of semantic features involve gradience. These findings are challenging for grammatical theory: are we justified to assume the existence of discrete verb classes or do the established properties indicate scalar dimensions of meaning? Based on two empirical studies – an acceptability study and a corpus study – the present article examines the role of agentivity in distinguishing verb classes and in predicting the syntactic behavior of verbs in German. Acceptability data show that the diagnostics of agentivity involve gradience, which cannot be reduced to random sources of variation. However, a comparison of scalar vs. categorical models of agentivity based on these diagnostics reveals that the syntactic variation in word order found in written corpus data is best accounted for through a model that assumes a binary division into a ±agentive and a non-agentive verb class.