Joint RNN-Based Greedy Parsing and Word Composition

Joël Legrand; Ronan Collobert

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arXiv · 1412.7028

Joint RNN-Based Greedy Parsing and Word Composition

Joël Legrand·Ronan Collobert

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Abstract

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.

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Joël Legrand, Ronan Collobert. 2014-12-22. Joint RNN-Based Greedy Parsing and Word Composition. https://arxiv.org/abs/1412.7028

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