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Convolutional Neural Networks for Sentence Classification-卷积神经网络的句子分类

我们报告了一系列实验,卷积神经网络(CNN)训练在预处理的词矢量上,用于句子级分类任务。 我们展示了一个简单的CNN,具有很少的超参数调整和静态矢量,可以在多个基准上实现出色的结果。 通过微调学习任务特定的向量可以进一步提高性能。 我们另外提出了对架构的简单修改,以允许使用任务特定和静态向量。 本文讨论的CNN模型改善了7项任务中的4项,其中包括情绪分析和问题分类。

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.

https://arxiv.org/abs/1408.5882

原创文章,作者:fendouai,如若转载,请注明出处:http://www.buluo360.com/2017/08/27/convolutional-neural-networks-for-sentence-classification/

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