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NIPS
2003

Online Learning via Global Feedback for Phrase Recognition

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Online Learning via Global Feedback for Phrase Recognition
We present a system to recognize phrases based on perceptrons, and a global online learning algorithm to train them together. The recognition strategy applies learning in two layers: a filtering layer, which reduces the search space by identifying plausible phrase candidates, and a ranking layer, which discriminatively builds the optimal phrase structure. We provide a recognition-based feedback rule which reflects to each local function its committed errors from a global point of view, and allows to train them together online as perceptrons. Experimentation on a syntactic parsing problem, the recognition of clause hierarchies, gives state-of-theart results and evinces the advantages of our global training method over optimizing each function locally, as in the traditional approach.
Xavier Carreras, Lluís Màrquez
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Xavier Carreras, Lluís Màrquez
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