CNN for Text Classification
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CNN forText Classification describes how convolutional neural networks (CNNs) can be used for text classification. CNNs apply convolution operations to input data to identify patterns. For natural language processing, CNNs convolve filters over word vectors or embeddings to analyze n-grams. The model is adapted from a 2014 paper and applies convolution and max pooling layers to classify sentences.
CNN for Text Classification
- 1. CNN forText Classification Tim Lee andWill Kelly
- 2. What are Convolutions? A convolution is an integral that expresses the amount of overlap of one function as it is shifted over another function Can be thought as "blending" functions Pictures found on Christopher Olah's blog, originally from Wikipedia 2 Definition from Wolfram Alpha's page on convolution 1
- 3. How does this work? ComputerVision NLP Word2Vec GloVe One-hot Images via WildML 3
- 4. Convolution Layers Picture found on Christopher Olah's blog 4
- 5. More complexity & Max-pooling Picture found on Christopher Olah's blog 4 Image via WildML 3
- 6. The Model Adapted from KimYoon's 2014 paper, Convolutional Neural Networks for Sentence Classification 5
- 7. References ? 1. http://mathworld.wolfram.com/Convolution.html ? 2. https://en.wikipedia.org/wiki/Convolution#/media/File:Convolutio n_of_box_signal_with_itself2.gif ? 3. http://www.wildml.com/2015/11/understanding-convolutional- neural-networks-for-nlp/ ? 4. http://colah.github.io/posts/2014-07-Conv-Nets-Modular/ ? 5. http://www.wildml.com/2015/12/implementing-a-cnn-for-text- classification-in-tensorflow/