In support of multiword unit classifications: Corpus and human rating data validate phraseological classifications of three different multiword unit types

Abstract

Multiword units (MWUs) are word combinations which sit within the continuum of formulaic language. Many experimental studies have focused on the online processing of MWUs by native and non-native speakers, and the processing of idioms in particular. However, some studies use a mix of various MWU subtypes, while other studies have varying definitions for the same subtypes. For results from MWU studies to be useful to theories of language processing, storage and access, clearer classifications are needed for MWU subtypes. This study aims to empirically validate MWU categories as described by certain phraseologists in the European tradition. This will be done using MWUs from the British National Corpus, from across the continuum of frequent to infrequent occurrence and co-occurrence. Hence, in this paper I will describe the empirical findings that may validate the classifications for MWU categories of restricted collocations, idioms, and lexical bundles, using corpus-based measures and human ratings.

Collocational Processing in Light of the Phraseological Continuum Model: Does Semantic Transparency Matter?

“…To the best of our knowledge, however, only one study has investigated potential differences between these types of lexical composites, in terms of how users perceive them. Columbus (), using human ratings of idioms and collocations, found that the mean ratings for familiarity, semantic transparency, and frequency could differentiate between these two word combination categories (and lexical bundles as well). However, no online processing task was used.…”

Is All Formulaic Language Created Equal? Unpacking the Processing Advantage for Different Types of Formulaic Sequences

“…She concluded that these differences may not be the result of the different subtypes per se, but that different variables relevant to each type produce different effects. Columbus (2013) went on to show that both corpus data and human ratings can reliably distinguish between subtypes, using measures such as frequency, familiarity, and perceived transparency. How these factors influence online processing remains to be explored, although as noted earlier, Gyllstad and Wolter (2016) showed that both transparency and frequency affected reaction times in their phrasal decision task.…”

Construction Repetition Reduces Information Rate in Dialogue

“…Lexicalised constructions can be classified according to multiple criteria (Titone and Connine, 1994;Wray, 2002;Columbus, 2013), including those listed below. --------------------------------------------------------------------------- -----------------------------------------------------------------------------------p values calculated using Satterthwaite d.f.…”