Convolutional Neural Networks for Sentence Classification
Open accessAbstract
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.
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One-dimensional CNN can be understood as extracting predictive n-gram features and encoding these features in a latent representation.
“When modeling sequential data, one-dimensional CNN can be understood intuitively as extracting predictive n-gram features and encoding these features in a latent representation.”
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- Authors
- Kim, Y.
- Affiliations
- New York University (US)
- Corresponding author
- Kim, Yoon
- ORCID
- Kim, Yoon
- Venue
- Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2014
- Pages
- 1746-1751
- OpenAlex
- W1832693441
- Cited by
- 14,831
- References
- 33
- Citations by year
- 2026: 565 · 2025: 910 · 2024: 1113 · 2023: 1406 · 2022: 1592 · 2021: 2115 · 2020: 2260 · 2019: 2134 · 2018: 1532 · 2017: 755 · 2016: 334 · 2015: 115
- FWCI
- 236.23
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- convolutional neural networks, sentence classification, pre-trained word vectors, static embeddings, fine-tuning, sentiment analysis, question classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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