Paper: Efficient Inference of CRFs for Large-Scale Natural Language Data

ACL ID P09-2071
Title Efficient Inference of CRFs for Large-Scale Natural Language Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2009
Authors

This paper presents an efficient inference algo- rithm of conditional random fields (CRFs) for large-scale data. Our key idea is to decompose the output label state into an active set and an inactive set in which most unsupported tran- sitions become a constant. Our method uni- fies two previous methods for efficient infer- ence of CRFs, and also derives a simple but robust special case that performs faster than exact inference when the active sets are suffi- ciently small. We demonstrate that our method achieves dramatic speedup on six standard nat- ural language processing problems.