Yoon Kim
yhk255@nyu.edu
September 24, 2014

Code for:

Convolutional Neural Networks for Sentence Classification
EMNLP 2014
http://arxiv.org/abs/1408.5882

This runs the model on Pang and Lee's movie review dataset (MR in the paper).
Please cite the original paper when using the data.

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.