%0 Generic %1 kalchbrenner2014convolutional %A Kalchbrenner, Nal %A Grefenstette, Edward %A Blunsom, Phil %D 2014 %K deep_learning thema thema:sentence_modeling %T A Convolutional Neural Network for Modelling Sentences %U http://arxiv.org/abs/1404.2188 %X 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.

@misc{kalchbrenner2014convolutional, 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.}, added-at = {2018-03-16T13:39:35.000+0100}, author = {Kalchbrenner, Nal and Grefenstette, Edward and Blunsom, Phil}, biburl = {https://www.bibsonomy.org/bibtex/2cd4f4fcc25ac2ae7bf5a55d490017f19/dallmann}, description = {A Convolutional Neural Network for Modelling Sentences}, interhash = {81260bd862d992b0c3ed72c01a035008}, intrahash = {cd4f4fcc25ac2ae7bf5a55d490017f19}, keywords = {deep_learning thema thema:sentence_modeling}, note = {cite arxiv:1404.2188}, timestamp = {2018-03-16T13:39:35.000+0100}, title = {A Convolutional Neural Network for Modelling Sentences}, url = {http://arxiv.org/abs/1404.2188}, year = 2014 }