CNN(Convolutional Neural Networks)\uc740 \uc774\ubbf8\uc9c0 \ubd84\ub958\uc5d0 \ub192\uc740 \uc131\ub2a5\uc744 \ubc1c\ud718\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc774\ub098 \uc774 \uc678\uc5d0\ub3c4 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\uc911\uc5d0 \ud558\ub098\uac00 \ud14d\uc2a4\ud2b8\ub97c \ubd84\ub958\ud558\ub294 \ubb38\uc81c\uc785\ub2c8\ub2e4.<\/p>\n\n\n\n
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Convolutional Neural Networks for Sentence Classification<\/p>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.<\/cite><\/blockquote>\n\n\n\n