Connecting morphosyntax and lexical semantics with Elastic Net regression KU Leuven
This project proposes to use regularization methods from machine
learning, more specifically Elastic Net regression (and its siblings
Ridge and Lasso), to look into lexical semantic effects in
morphosyntactic alternances. These regularization techniques apply
shrinkage to the coefficients and can thus be used for variable
selection, especially when the number of predictors is very large. In
variationist studies, ...