BibTeX
@MISC{Kim_convolutionalneural,
author = {Yoon Kim},
title = {Convolutional Neural Networks for Sentence Classification},
year = {}
}
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Abstract
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vec-tors for sentence-level classification tasks. We show that a simple CNN with lit-tle hyperparameter tuning and static vec-tors achieves excellent results on multi-ple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the ar-chitecture 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. 1
Keyphrases
convolutional neural network sentence classification sentence-level classification task task-specific vector fine-tuning offer multi-ple benchmark simple cnn sentiment analysis lit-tle hyperparameter tuning simple modification cnn model question classification pre-trained word vec-tors static vector static vec-tors achieves excellent result
