Investigating Lexical Effects in Syntax with Regularized Regression (Lasso)

Abstract

Within usage-based theory, notably in construction grammar though also elsewhere, the role of the lexicon and of lexically-specific patterns in morphosyntax is well recognized. The methodology, however, is not always sufficiently suited to get at the details, as lexical effects are difficult to study under what are currently the standard methods for investigating grammar empirically. In this short article, we propose a method from machine learning: regularized regression (Lasso) with k-fold cross-validation, and compare its performance with a Distinctive Collexeme Analysis.