Discriminative Learning of Probabilistic Sequence Models for Sequence Labeling Problems
نویسندگان
چکیده
The problem of labeling (or segmenting) sequences is very important in many applications such as part-of-speech tagging in natural language processing, multimodal object detection in computer vision, and DNA/protein structure prediction in bioinformatics. Conditional Random Fields (CRFs) of [1] are known to be the best sequence models ever for the problem. CRF is a conditional model, P (s|y), infered from a (joint) log-linear model P (s,y), which is derived from Maximum Entropy (ME) principle. Since the dual formulation of ME results in Maximum Likelihood (ML) objective, it is natural to learn CRF via Conditional Likelihood Maximization (CML). Recently, diverse numerical optimization methods for CML (e.g., IIS, Conjugate Gradient, and Quasi-Newton) together with their convergence rates as well as prediction performances have been studied (e.g., [2]).
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