Phrase recognition by filtering and ranking with perceptrons

We present a phrase recognition system based on perceptrons, and an online learning algorithm to train them together. The recognition strategy applies learning in two layers, first at word level, to filter words and form phrase candidates, second at phrase level, to rank phrases and select the optimal ones. We provide a global feedback rule which reflects the dependencies among perceptrons and allows to train them together online. Experimentation on Partial Parsing problems and Named Entity Extraction gives state-of-the-art results on the CoNLL public datasets. We also provide empirical evidence that training the functions together is clearly better than training them separately, as in the conventional approach.