Deep Learning for Efficient Discriminative Parsing

author: Ronan Collobert, NEC Laboratories America, Inc.
published: May 6, 2011,   recorded: April 2011,   views: 16903


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We propose a new fast purely discriminative algorithm for natural language parsing, based on a "deep" recurrent convolutional graph transformer network (GTN). Assuming a decomposition of a parse tree into a stack of "levels", the network predicts a level of the tree taking into account predictions of previous levels. Using only few basic text features, we show similar performance (in F1 score) to existing pure discriminative parsers and existing "benchmark" parsers (like Collins parser, probabilistic context-free grammars based), with a huge speed advantage.

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Comment1 jaya, May 29, 2016 at 8:37 p.m.:

i am new to deep learning ,to start with what i should read

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