Tensorflow Implementation of "Recurrent Convolutional Neural Network for Text Classification" (AAAI 2015)
Tensorflow implementation of "Recurrent Convolutional Neural Network for Text Classification".
positive data is located in <U>"data/rt-polaritydata/rt-polarity.pos"</U>.
negative data is located in <U>"data/rt-polaritydata/rt-polarity.neg"</U>.
"GoogleNews-vectors-negative300" is used as pre-trained word2vec model.
Display help message:
python train.py --help
Train Example:
python train.py --cell_type "lstm" \
-pos_dir "data/rt-polaritydata/rt-polarity.pos" \
-neg_dir "data/rt-polaritydata/rt-polarity.neg"\
-word2vec "GoogleNews-vectors-negative300.bin"
Movie Review dataset has no test data.
If you want to evaluate, you should make test dataset from train data or do cross validation. However, cross validation is not implemented in my project.
The bellow example just use full rt-polarity dataset same the train dataset.
Evaluation Example:
python eval.py \
-pos_dir "data/rt-polaritydata/rt-polarity.pos" \
-neg_dir "data/rt-polaritydata/rt-polarity.neg" \
-checkpoint_dir "runs/1523902663/checkpoints"