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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 neural networks (CNN) utilize layers with convolving lters that are applied to local features (LeCun et al., 1998). Originally invented for computer vision, CNN models have subsequently been shown to be effective for NLP and have achieved excellent results in semantic parsing (Yih et al., 2014), search query retrieval

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