Paper: Supervised Model Learning with Feature Grouping based on a Discrete Constraint

ACL ID P13-2004
Title Supervised Model Learning with Feature Grouping based on a Discrete Constraint
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

This paper proposes a framework of super- vised model learning that realizes feature grouping to obtain lower complexity mod- els. The main idea of our method is to integrate a discrete constraint into model learning with the help of the dual decom- position technique. Experiments on two well-studied NLP tasks, dependency pars- ing and NER, demonstrate that our method can provide state-of-the-art performance even if the degrees of freedom in trained models are surprisingly small, i.e., 8 or even 2. This significant benefit enables us to provide compact model representation, which is especially useful in actual use.