Paper: A Hybrid Markov/Semi-Markov Conditional Random Field For Sequence Segmentation

ACL ID W06-1655
Title A Hybrid Markov/Semi-Markov Conditional Random Field For Sequence Segmentation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

Markov order-1 conditional random fields (CRFs) and semi-Markov CRFs are two popular models for sequence segmenta- tion and labeling. Both models have ad- vantages in terms of the type of features they most naturally represent. We pro- pose a hybrid model that is capable of rep- resenting both types of features, and de- scribe efficient algorithms for its training and inference. We demonstrate that our hybrid model achieves error reductions of 18% and 25% over a standard order-1 CRF and a semi-Markov CRF (resp). on the task of Chinese word segmentation. We also propose the use of a powerful fea- ture for the semi-Markov CRF: the log conditional odds that a given token se- quence constitutes a chunk according to a generative model, which reduces error by an additional 13%. Our best system a...