Joint RNN-Based Greedy Parsing and Word Composition
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.
Cite
Text
Legrand and Collobert. "Joint RNN-Based Greedy Parsing and Word Composition." International Conference on Learning Representations, 2015.Markdown
[Legrand and Collobert. "Joint RNN-Based Greedy Parsing and Word Composition." International Conference on Learning Representations, 2015.](https://mlanthology.org/iclr/2015/legrand2015iclr-joint/)BibTeX
@inproceedings{legrand2015iclr-joint,
title = {{Joint RNN-Based Greedy Parsing and Word Composition}},
author = {Legrand, Joël and Collobert, Ronan},
booktitle = {International Conference on Learning Representations},
year = {2015},
url = {https://mlanthology.org/iclr/2015/legrand2015iclr-joint/}
}