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...
Incremental parsing techniques such as shift-reduce have gained popularity thanks to their efficiency, but there remains a major problem: the search is greedy and only explores a ...
We present a novel semi-supervised training algorithm for learning dependency parsers. By combining a supervised large margin loss with an unsupervised least squares loss, a discr...
Polarized dependency (PD-) grammars are proposed as a means of efficient treatment of discontinuous constructions. PD-grammars describe two kinds of dependencies : local, explicit...
Abstract. A top-down parsing algorithm has been constructed to accommodate any form of ambiguous context-free grammar, augmented with semantic rules with arbitrary attribute depend...