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Convolutional Neural Networks for Sentence Classification

arXiv 1408.5882 ↗First posted 25 Aug 2014cs.CL cs.NEcited by 1,127

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Abstract (arXiv)

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.

Key facts. Cited by 1,127 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Aug 2014.

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Citing papers per year: 2018: 3; 2019: 1; 2020: 1; 2021: 400; 2022: 251; 2023: 177; 2024: 133; 2025: 97; 2026: 64.

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