Xavier Carreras, Lluís Màrquez
This work presents an architecture based on perceptrons to recognize phrase structures, and an online learning algorithm to train the percep- trons together and dependently. The recognition strategy applies learning in two layers: a ﬁltering layer, which reduces the search space by identi- fying plausible phrase candidates, and a ranking layer, which recursively builds the optimal phrase structure. We provide a recognition-based feed- back rule which reﬂects to each local function its committed errors from a global point of view, and allows to train them together online as percep- trons. Experimentation on a syntactic parsing problem, the recognition of clause hierarchies, improves state-of-the-art results and evinces the advantages of our global training method over optimizing each function locally and independently.