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Convolutional Neural Networks for Sentence Classification

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746 1751,October 25-29, 2014, Doha, 2014 Association for Computational LinguisticsConvolutional Neural Networks for Sentence ClassificationYoon KimNew York report on a series of experiments withconvolutional Neural Networks (CNN)trained on top of pre-trained word vec-tors for Sentence -level Classification show that a simple CNN with lit-tle hyperparameter tuning and static vec-tors achieves excellent results on multi-ple task-specificvectors through fine-tuning offers furthergains in additionallypropose a simple modification to the ar-chitecture to allow for the use of bothtask-specific and static vectors.

Convolutional layer with multiple filter widths and feature maps Max -over -time pooling Fully connected layer with dropout and softmax output Figure 1: Model architecture with two channels for an example sentence. necessary) is represented as x 1: n = x 1 x 2::: x n; (1) is the concatenation operator. In gen-

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