Convolutional Neural Networks for Sentence Classification

Yoon Kim

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arXiv · 1408.5882

Convolutional Neural Networks for Sentence Classification

Yoon Kim

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Abstract

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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BibTeX RIS

Yoon Kim. 2014-09-03. Convolutional Neural Networks for Sentence Classification. https://arxiv.org/abs/1408.5882

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