Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
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Neural Computation
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AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Multilingual joint parsing of syntactic and semantic dependencies with a latent variable model
Computational Linguistics
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We propose a generative model based on Temporal Restricted Boltzmann Machines for transition based dependency parsing. The parse tree is built incrementally using a shift-reduce parse and an RBM is used to model each decision step. The RBM at the current time step induces latent features with the help of temporal connections to the relevant previous steps which provide context information. Our parser achieves labeled and unlabeled attachment scores of 88.72% and 91.65% respectively, which compare well with similar previous models and the state-of-the-art.