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

ACL ID E14-1046
Title Improving the Lexical Function Composition Model with Pathwise Optimized Elastic-Net Regression
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper, we show that the lexical function model for composition of dis- tributional semantic vectors can be im- proved by adopting a more advanced re- gression technique. We use the pathwise coordinate-descent optimized elastic-net regression method to estimate the compo- sition parameters, and compare the result- ing model with several recent alternative approaches in the task of composing sim- ple intransitive sentences, adjective-noun phrases and determiner phrases. Experi- mental results demonstrate that the lexical function model estimated by elastic-net re- gression achieves better performance, and it provides good qualitative interpretabil- ity through sparsity constraints on model parameters.