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Linguistic structures exhibit a rich array of global phenomena, howevercommonly used Markov models are unable to adequately describe these phenomenadue to their strong locality assumptions. We propose a novel hierarchical modelfor structured prediction over sequences and trees which exploits globalcontext by conditioning each generation decision on an unbounded context ofprior decisions. This builds on the success of Markov models but withoutimposing a fixed bound in order to better represent global phenomena. Tofacilitate learning of this large and unbounded model, we use a hierarchicalPitman-Yor process prior which provides a recursive form of smoothing. Wepropose prediction algorithms based on A* and Markov Chain Monte Carlosampling. Empirical results demonstrate the potential of our model compared tobaseline finite-context Markov models on part-of-speech tagging and syntacticparsing.

Structured Prediction of Sequences and Trees using Infinite Contexts