Convolutional Neural Networks for Sentence Classification

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.