Empirical Co-occurrence Rate Networks for Sequence Labeling

Structured prediction has wide applications in many areas. Powerful and popular models for structured prediction have been developed. Despite the successes, they suffer from some known problems: (i) Hidden Markov models are generative models which suffer from the mismatch problem. Also it is difficult to incorporate overlapping, non-independent features into a hidden Markov model explicitly. (ii) Conditional Markov models suffer from the label bias problem. (iii) Conditional Random Fields (CRFs) overcome the label bias problem by global normalization. But the global normalization of CRFs can be expensive which prevents CRFs from applying to big data. In this paper, we propose the Empirical Co-occurrence Rate Networks (ECRNs) for sequence labeling. ECRNs are discriminative models, so ECRNs overcome the problems of HMMs. ECRNs are also immune to the label bias problem even though they are locally normalized. To make the estimation of ECRNs as fast as possible, we simply use the empirical distributions as the estimation of parameters. Experiments on two real-world NLP tasks show that ECRNs reduce the training time radically while obtain competitive accuracy to the state-of-the-art models.