Convolutional Neural Networks for Sentence Classification

Reproduced code for 4 models in the paper [Convolutional Neural Networks for Sentence Classification](EMNLP 2014) with all the datasets.

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

Requirements

Code is written in Python (2.7) and requires Theano (0.7).

Using the pre-trained word2vec vectors will also require downloading the binary file from https://code.google.com/p/word2vec/

Data Preprocessing

To process the raw data, run

python process_data.py path

where path points to the word2vec binary file (i.e. GoogleNews-vectors-negative300.bin file). This will create a pickle object called mr.p in the same folder, which contains the dataset in the right format.

Running the models (CPU)

Example commands:

THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python conv_net_sentence.py -nonstatic -rand
THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python conv_net_sentence.py -static -word2vec
THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python conv_net_sentence.py -nonstatic -word2vec

This will run the CNN-rand, CNN-static, and CNN-nonstatic models respectively in the paper.

Using the GPU

GPU will result in a good 10x to 20x speed-up, so it is highly recommended. To use the GPU, simply change device=cpu to device=gpu (or whichever gpu you are using). For example:

THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python conv_net_sentence.py -nonstatic -word2vec

Example output

CPU output:

epoch: 1, training time: 219.72 secs, train perf: 81.79 %, val perf: 79.26 %
epoch: 2, training time: 219.55 secs, train perf: 82.64 %, val perf: 76.84 %
epoch: 3, training time: 219.54 secs, train perf: 92.06 %, val perf: 80.95 %

GPU output:

epoch: 1, training time: 16.49 secs, train perf: 81.80 %, val perf: 78.32 %
epoch: 2, training time: 16.12 secs, train perf: 82.53 %, val perf: 76.74 %
epoch: 3, training time: 16.16 secs, train perf: 91.87 %, val perf: 81.37 %