Yoon Kim yhk255@nyu.edu September 24, 2014 Code for: Convolutional Neural Networks for Sentence Classification EMNLP 2014 http://arxiv.org/abs/1408.5882 Runs the model on Pang and Lee's movie review dataset (MR in the paper). Please cite the original paper when using the data. Runs on Python 2.7 and Theano 0.6 The code does not use GPUs. It should run much faster if you have a good GPU. See http://deeplearning.net/software/theano/tutorial/using_gpu.html for information on how to make it use GPUs. (People have have reported up to 20x speed-up by modifying this code to make use of a modern GPU, so this is a must if working with larger corpora.) Instructions: 1. with all the files in folder, run python process_data.py -path where -path points to the word2vec binary file (i.e. GoogleNews-vectors-negative300.bin file). Downloadable at https://code.google.com/p/word2vec/ This will create a pickle object called "mr.p" in the same folder, which contains the dataset in the right format. 2. run python conv_net_sentence.py -nonstatic -rand python conv_net_sentence.py -static -word2vec python conv_net_sentence.py -nonstatic -word2vec This will run the CNN-rand, CNN-static, and CNN-nonstatic models respectively in the paper. *Note: Step 1 will create the dataset with different fold-assignments than was used in the paper. You should still be getting a CV score of >81% with CNN-nonstatic model, though.