Convolutional Neural Networks for Sentence Classification Convolutional Neural Networks for Sentence Classification
Paper summary #### Introduction * The paper demonstrates how simple CNNs, built on top of word embeddings, can be used for sentence classification tasks. * [Link to the paper]( * [Implementation]( #### Architecture * Pad input sentences so that they are of the same length. * Map words in the padded sentence using word embeddings (which may be either initialized as zero vectors or initialized as word2vec embeddings) to obtain a matrix corresponding to the sentence. * Apply convolution layer with multiple filter widths and feature maps. * Apply max-over-time pooling operation over the feature map. * Concatenate the pooling results from different layers and feed to a fully-connected layer with softmax activation. * Softmax outputs probabilistic distribution over the labels. * Use dropout for regularisation. #### Hyperparameters * RELU activation for convolution layers * Filter window of 3, 4, 5 with 100 feature maps each. * Dropout - 0.5 * Gradient clipping at 3 * Batch size - 50 * Adadelta update rule. #### Variants * CNN-rand * Randomly initialized word vectors. * CNN-static * Uses pre-trained vectors from word2vec and does not update the word vectors. * CNN-non-static * Same as CNN-static but updates word vectors during training. * CNN-multichannel * Uses two set of word vectors (channels). * One set is updated and other is not updated. #### Datasets * Sentiment analysis datasets for Movie Reviews, Customer Reviews etc. * Classification data for questions. * Maximum number of classes for any dataset - 6 #### Strengths * Good results on benchmarks despite being a simple architecture. * Word vectors obtained by non-static channel have more meaningful representation. #### Weakness * Small data with few labels. * Results are not very detailed or exhaustive.
Convolutional Neural Networks for Sentence Classification
Kim, Yoon
arXiv e-Print archive - 2014 via Bibsonomy
Keywords: dblp

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