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11 - Testing the Predictions of Word Grammar, the Minimalist Programme and the Matrix Language Frame Model for German/English Mixed Determiner–Noun Constructions

Published online by Cambridge University Press:  19 December 2024

By
Eva Duran Eppler ,
Adrian Luescher  and
Margaret Deuchar
Edited by
Eva Duran Eppler ,
Nikolas Gisborne  and
Andrew Rosta
Eva Duran Eppler
Affiliation:
Roehampton University, London
Nikolas Gisborne
Affiliation:
University of Edinburgh
Andrew Rosta
Affiliation:
University of Central Lancashire, Preston
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Summary

This paper presents a comparative evaluation of Word Grammar (WG), the Minimalist Programme (MP), and the Matrix Language Frame model (MLF) regarding their predictions of possible combinations in a corpus of German–English mixed determiner–noun constructions. WG achieves the highest accuracy score. The comparison furthermore revealed a difference in accuracy of the predictions between the three models and a significant difference between WG and the MP. The analysis suggests that these differences depend on assumptions made by the models and the mechanisms they employ. The difference in accuracy between the models, for example, can be attributed to the MLF being concerned with agreement in language membership between the verb and the subject DP/NP of the clause. The significant difference between WG and the MP can be attributed to the distinct roles features play in the two syntactic theories and how agreement is handled. Based on the results, we draw up a list of characteristics of feature accounts that are empirically most adequate for the mixed determiner–noun constructions investigated and conclude that the syntactic theory that incorporates most of them is WG (Hudson 2007, 2010).

Keywords

Word GrammarMinimalist ProgrammeMatrix Language Frame modelcode-switchingdeterminer-noun constructionspredictionsaccuracy (of the predictions)featuresagreementGermanEnglish
Type
Chapter
Information
Word Grammar, Cognition and Dependency , pp. 252 - 275
Publisher: Cambridge University Press
Print publication year: 2025

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