Conditional Random Fields (CRFs) is a useful technique to label sequential data. Due to considering all label combinations of a sequence, CRFs' training and testing are time consuming. In this work, we consider a Newton method for training CRFs because of its possible fast final convergence. The computational bottleneck is on the Hessian-vector product. We propose a novel dynamic programming technique to calculate it in polynomial time.
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