Markov order-1 conditional random fields (CRFs) and semi-Markov CRFs are two popular models for sequence segmentation and labeling. Both models have advantages in terms of the type of features they most naturally represent. We propose a hybrid model that is capable of representing both types of features, and describe 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 feature for the semi-Markov CRF: the log conditional odds that a given token sequence constitutes a chunk according to a generative model, which reduces error by an additional 13%. Our best system achieves 96.8% F-measure, the highest reported score on this test set.
REF proposed a semi-CRF model.
A Hybrid Markov/Semi-Markov Conditional Random Field For Sequence Segmentation
Conference On Empirical Methods In Natural Language Processing
Computer Science