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Wide coverage natural language processing using kernel methods and neural networks for structured data
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DOI：10.1016/j.patrec.2005.03.011.png)
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Abstract
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Convolution kernels and recursive neural networks are both suitable approaches for supervised learning when the input is a discrete structure like a labeled tree or graph. We compare these techniques in two natural language problems. In both problems, the learning task consists in choosing the best alternative tree in a set of candidates. We report about an empirical evaluation between the two methods on a large corpus of parsed sentences and speculate on the role played by the representation and the loss function. (c) 2005 Elsevier B.V. All rights reserved.
Keywords:
convolution kernels
recursive neural networks
learning preferences
natural language
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