NLP Reading Group: Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks

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• Wenduan Xu
• Thursday 15 March 2012, 12:00-13:00
• GS15, Computer Laboratory.

If you have a question about this talk, please contact Marek Rei.

This week Wenduan will be presenting:

Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks. Richard Socher, Christopher D. Manning, Andrew Y. Ng. Deep Learning and Unsupervised Feature Learning Workshop – NIPS 2010

Natural language parsing has typically been done with small sets of discrete categories such as NP and VP, but this representation does not capture the full syntactic nor semantic richness of linguistic phrases, and attempts to improve on this by lexicalizing phrases only partly address the problem at the cost of huge feature spaces and sparseness. To address this, we introduce a recursive neural network architecture for jointly parsing natural language and learning vector space representations for variable-sized inputs. At the core of our architecture are context-sensitive recursive neural networks (CRNN). These networks can induce distributed feature representations for unseen phrases and provide syntactic information to accurately predict phrase structure trees. Most excitingly, the representation of each phrase also captures semantic information: For instance, the phrases “decline to comment” and “would not disclose the terms” are close by in the induced embedding space. Our current system achieves an unlabeled bracketing F-measure of 92.1% on the Wall Street Journal dataset for sentences up to length 15.

PDF : http://www.cs.stanford.edu/people/ang/papers/nipsdlufl10-LearningContinuousPhraseRepresentations.pdf

March 15, 12:00, GS15

This talk is part of the Natural Language Processing Reading Group series.

This talk is included in these lists:

• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• GS15, Computer Laboratory
• Guy Emerson's list
• Natural Language Processing Reading Group

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