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Gaussian Process Classification for Segmenting and Annotating Sequences AbstractMany real-world classification tasks involve the prediction of multiple, inter-dependent class labels. A prototypical case of this sort deals with sequences of observations for which a corresponding sequence of labels needs to be predicted. Such problems arise naturally in the context of annotating and segmenting observation sequences. This paper generalizes Gaussian Process classification to deal with these types of problems in a way that allows to take dependencies between (neighboring) labels into account. Our approach is motivated by the desire to retain a rigorous probabilistic semantics, while overcoming limitations of parametric methods like Conditional Random Fields, which exhibit conceptual and computational difficulties in high-dimensional input spaces. Experiments on Named Entity Recognition and Pitch Accent prediction tasks demonstrate the competitiveness of our approach.
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