Hidden Dynamic Probabilistic Models for Labeling Sequence Data

Xiaofeng Yu, Wai Lam

We propose a new discriminative framework, namely Hidden Dynamic Conditional Random Fields (HDCRFs), for building probabilistic models which can capture both internal and external class dynamics to label sequence data. We introduce a small number of hidden state variables to model the sub-structure of a observation sequence and learn dynamics between different class labels. An HDCRF offers several advantages over previous discriminative models and is attractive both, conceptually and computationally. We performed experiments on three well-established sequence labeling tasks in natural language, including part-of-speech tagging, noun phrase chunking, and named entity recognition. The results demonstrate the validity and competitiveness of our model. In addition, our model compares favorably with current state-of-the-art sequence labeling approach, Conditional Random Fields (CRFs), which can only model the external dynamics.

Subjects: 12. Machine Learning and Discovery; 13. Natural Language Processing

Submitted: Apr 15, 2008


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