Paper: Kernel Slicing: Scalable Online Training with Conjunctive Features

ACL ID C10-1140
Title Kernel Slicing: Scalable Online Training with Conjunctive Features
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

This paper proposes an efficient online method that trains a classifier with many conjunctive features. We employ kernel computation called kernel slicing, which explicitly considers conjunctions among frequent features in computing the poly- nomial kernel, to combine the merits of linear and kernel-based training. To im- prove the scalability of this training, we reuse the temporal margins of partial fea- ture vectors and terminate unnecessary margin computations. Experiments on de- pendency parsing and hyponymy-relation extraction demonstrated that our method could train a classifier orders of magni- tude faster than kernel-based online learn- ing, while retaining its space efficiency.