In this paper, we provide a study on the use of tree kernels to encode syntactic parsing information in natural language learning. In particular, we propose a new convolution kerne...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
We present a novel transition system for dependency parsing, which constructs arcs only between adjacent words but can parse arbitrary non-projective trees by swapping the order o...
Abstract. Hierarchical and recursive state machines are suitable abstract models for many software systems. In this paper we extend a model recently introduced in literature, by al...
Salvatore La Torre, Margherita Napoli, Mimmo Paren...
Deterministic parsing has emerged as an effective alternative for complex parsing algorithms which search the entire search space to get the best probable parse tree. In this pape...