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Online Learning via Global Feedback for Phrase Recognition
2003
Neural Information Processing Systems
This work presents an architecture based on perceptrons to recognize phrase structures, and an online learning algorithm to train the perceptrons together and dependently. 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 recursively builds the optimal phrase structure. We provide a recognition-based feedback rule which reflects to each local function its committed
dblp:conf/nips/CarrerasM03
fatcat:adpx4eppsfegbirkls76rznhhi