Search-based structured prediction |
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Authors: | III" target="_blank">Hal DauméIII John Langford Daniel Marcu |
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Affiliation: | (1) School of Computing, University of Utah, Salt Lake City, UT 84112, USA;(2) Yahoo! Research Labs, New York, NY 10011, USA;(3) Information Sciences Institute, Marina del Rey, CA 90292, USA |
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Abstract: | We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology,
and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier
may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted structure, Searn is able to learn prediction functions for any loss function and any class of features. Moreover, Searn comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies good
performance on the structured prediction problem. |
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Keywords: | Structured prediction Search Reductions |
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