Modelling Differences in Multiword Expression Use Across Proficiency Levels in a Test of Spoken English

Abstract Despite the increased research on multiword expressions (MWEs) in the past two decades, there is a lack of theory that can explain the often-contradictory findings of those studies. Therefore, the aim of this paper was to propose two models, (a) one explaining differences in MWE density produced by speakers and writers at different proficiency levels and (b) another explaining differences in the difficulty of acquisition of different MWE groups. Taken together, they should theoretically predict differences in phrase density of MWEs produced by language users of different proficiency levels, with any L1-L2 combination, for various MWE groups.This paper validated the models for use for the test takers of the KIT Speaking Test (L1 Japanese-L2 English). The data used came from a 222,777-word corpus containing 574 examinees’ responses across four proficiency groups. Findings show that the proposed models predicted the phrase density patterns very well for 12 different MWE groups. These models not only contribute to theory, but also have the practical application of helping identify the MWE groups that raters should focus on to differentiate between test takers, including where lower rates of use should not be incorrectly attributed to a lower score.

Authors

Institutions

Publication Details

Journal
Corpus-based Studies across Humanities
Published
2025-03-04
DOI
https://doi.org/10.1515/csh-2024-0030
Primary Topic
Second Language Acquisition and Learning
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modelling Differences in Multiword Expression Use Across Proficiency Levels in a Test of Spoken English

Ivy Chen, Katsunori Kanzawa
Corpus-based Studies across Humanities
Second Language Acquisition and Learning
article

Modelling Differences in Multiword Expression Use Across Proficiency Levels in a Test of Spoken English

Ivy Chen, Katsunori Kanzawa
article en

Abstract

Abstract Despite the increased research on multiword expressions (MWEs) in the past two decades, there is a lack of theory that can explain the often-contradictory findings of those studies. Therefore, the aim of this paper was to propose two models, (a) one explaining differences in MWE density produced by speakers and writers at different proficiency levels and (b) another explaining differences in the difficulty of acquisition of different MWE groups. Taken together, they should theoretically predict differences in phrase density of MWEs produced by language users of different proficiency levels, with any L1-L2 combination, for various MWE groups.This paper validated the models for use for the test takers of the KIT Speaking Test (L1 Japanese-L2 English). The data used came from a 222,777-word corpus containing 574 examinees’ responses across four proficiency groups. Findings show that the proposed models predicted the phrase density patterns very well for 12 different MWE groups. These models not only contribute to theory, but also have the practical application of helping identify the MWE groups that raters should focus on to differentiate between test takers, including where lower rates of use should not be incorrectly attributed to a lower score.

Corpus-based Studies across Humanities
The University of Melbourne (AU), Kyoto Institute of Technology (JP)
Japan Society for the Promotion of Science
Quality Education
Openalex Percentile: Top 96%
Second Language Acquisition and Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.