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A Convolutional Neural Network for Modelling Sentences

arXiv 1404.2188 ↗First posted 8 Apr 2014cs.CLcited by 163

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Abstract (arXiv)

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.

Key facts. Cited by 163 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2014.

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Citing papers per year: 2018: 1; 2019: 2; 2020: 0; 2021: 67; 2022: 37; 2023: 28; 2024: 15; 2025: 8; 2026: 5.

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