Improving the Lexical Function Composition Model with Pathwise Optimized Elastic-Net Regression

In this paper, we show that the lexical function model for composition of distributional semantic vectors can be improved by adopting a more advanced regression technique. We use the pathwise coordinate-descent optimized elastic-net regression method to estimate the composition parameters, and compare the resulting model with several recent alternative approaches in the task of composing simple intransitive sentences, adjective-noun phrases and determiner phrases. Experimental results demonstrate that the lexical function model estimated by elastic-net regression achieves better performance, and it provides good qualitative interpretability through sparsity constraints on model parameters.