Non-Projective Dependency Parsing via Latent Heads Representation (LHR)

6 Feb 2018 Matteo Grella Simone Cangialosi

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... (read more)

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Methods used in the Paper


METHOD TYPE
AutoEncoder
Generative Models