  This paper introduces a greedy parser based on neural networks, which
leverages a new compositional sub-tree representation. The greedy parser and
the compositional procedure are jointly trained, and tightly depends on
each-other. The composition procedure outputs a vector representation which
summarizes syntactically (parsing tags) and semantically (words) sub-trees.
Composition and tagging is achieved over continuous (word or tag)
representations, and recurrent neural networks. We reach F1 performance on par
with well-known existing parsers, while having the advantage of speed, thanks
to the greedy nature of the parser. We provide a fully functional
implementation of the method described in this paper.
