Measuring the semantic headedness of English blends with token-based semantic vector space modeling: a corpus-based study

Abstract

Abstract This article analyzes the semantic headedness of English blends with distributional semantics methods. The semantic head of a blend is the source word that transfers its semantic information to the blend as a whole. For example, a sitcom is a kind of comedy. But is FedEx a kind of express, and is wi-fi a kind of fidelity? We use corpus data and token-based semantic vector space modeling in order to address these questions. Specifically, we investigate whether Plag’s ternary division of endocentric, exocentric, and coordinative compounds based on semantic headedness can also be applied to English blends, and whether the general tendency of semantic right-headedness can be observed for all three subtypes. We analyze a dataset of fifty-five blends and their respective source words, using data from the Corpus of Contemporary American English and the English Web Corpus 2021. We measure the degree of semantic similarity between each blend and its two source words. The results show that for most endocentric blends, the hypothesis of semantic right-headedness holds true. At the same time, exocentric blends and coordinative blends are shown to behave differently. We conclude that Plag’s classification offers a useful point of departure for the semantic analysis of blends and that distributional semantics methods can provide new insights into their semantic behavior.