Max-Margin Parsing

We present a novel discriminative approach to parsing inspired by the large-margin criterion underlying sup- port vector machines. Our formulation uses a factor- ization analogous to the standard dynamic programs for parsing. In particular, it allows one to efficiently learn a model which discriminates among the entire space of parse trees, as opposed to reranking the top few candi- dates. Our models can condition on arbitrary features of input sentences, thus incorporating an important kind of lexical information without the added algorithmic com- plexity of modeling headedness. We provide an efficient algorithm for learning such models and show experimen- tal evidence of the model's improved performance over a natural baseline model and a lexicalized probabilistic context-free grammar.