Paper: Practical Very Large Scale CRFs

ACL ID P10-1052
Title Practical Very Large Scale CRFs
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

Conditional Random Fields (CRFs) are a widely-used approach for supervised sequence labelling, notably due to their ability to handle large description spaces and to integrate structural dependency be- tween labels. Even for the simple linear- chain model, taking structure into account implies a number of parameters and a computational effort that grows quadrati- cally with the cardinality of the label set. In this paper, we address the issue of train- ing very large CRFs, containing up to hun- dreds output labels and several billion fea- tures. Efficiency stems here from the spar- sity induced by the use of a lscript1 penalty term. Based on our own implementa- tion, we compare three recent proposals for implementing this regularization strat- egy. Our experiments demonstrate that very lar...