Disentangling modal meanings with distributional semantics

Abstract

AbstractThis article investigates the collocational behavior of English modal auxiliaries such as may and might with the aim of finding corpus-based measures that distinguish between different modal expressions and that allow insights into why speakers may choose one over another in a given context. The analysis uses token-based semantic vector space modeling (Heylen et al., 2015, Monitoring polysemy. Word space models as a tool for large-scale lexical semantic analysis. Lingua, 157: 153–72; Hilpert and Correia Saavedra, 2017, Using token-based semantic vector spaces for corpus-linguistic analyses: From practical applications to tests of theoretical claims. Corpus Linguistics and Linguistic Theory) in order to determine whether different modal auxiliaries can be distinguished in terms of their collocational profiles. The analysis further examines whether different senses of the same auxiliary exhibit divergent collocational preferences. The results indicate that near-synonymous pairs of modal expressions, such as may and might or must and have to, differ in their distributional characteristics. Also, different senses of the same modal expression, such as deontic and epistemic uses of may, can be distinguished on the basis of distributional information. We discuss these results against the background of previous empirical findings (Hilpert, 2016, Construction Grammar and its Application to English, 2nd edn. Edinburgh: Edinburgh University Press, Flach, in press, Beyond modal idioms and modal harmony: a corpus-based analysis of gradient idiomaticity in modal-adverb collocations. English Language and Linguistics) and theoretical issues such as degrees of grammaticalization (Correia Saavedra, 2019, Measurements of Grammaticalization: Developing a Quantitative Index for the Study of Grammatical Change. PhD Dissertation, Université de Neuchâtel) and the avoidance of synonymy (Bolinger, 1968, Entailment and the meaning of structures. Glossa, 2(2): 119–27).