  In this paper, we introduce a novel approach based on a bidirectional
recurrent autoencoder to perform globally optimized non-projective dependency
parsing via semi-supervised learning. The syntactic analysis is completed at
the end of the neural process that generates a Latent Heads Representation
(LHR), without any algorithmic constraint and with a linear complexity. The
resulting "latent syntactic structure" can be used directly in other semantic
tasks. The LHR is transformed into the usual dependency tree computing a simple
vectors similarity. We believe that our model has the potential to compete with
much more complex state-of-the-art parsing architectures.
