Paper: A Convolutional Neural Network for Modelling Sentences

ACL ID P14-1062
Title A Convolutional Neural Network for Modelling Sentences
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

The ability to accurately represent sen- tences is central to language understand- ing. We describe a convolutional architec- ture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pool- ing, a global pooling operation over lin- ear 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 ap- plicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment predic- tion, six-way question classification and Twitter sentiment prediction by distant su- pervision. The network achieve...