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A convolutional neural network for modelling sentences. (2014)
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| Venue: | In Proceedings of the 52th Annual Meeting of the Association for Computational Linguistics. |
| Citations: | 59 - 2 self |
BibTeX
@INPROCEEDINGS{Kalchbrenner14aconvolutional,
author = {Nal Kalchbrenner and Edward Grefenstette and Phil Blunsom},
title = {A convolutional neural network for modelling sentences.},
booktitle = {In Proceedings of the 52th Annual Meeting of the Association for Computational Linguistics.},
year = {2014}
}
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Abstract
Abstract The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.
