Convolutional Neural Networks for Sentence Classification
Train convolutional network for sentiment analysis. Based on "Convolutional Neural Networks for Sentence Classification" by Yoon Kim, link. Inspired by Denny Britz article "Implementing a CNN for Text Classification in TensorFlow", link.
For "CNN-rand" and "CNN-non-static" gets to 88-90%, and "CNN-static" - 85%
Some difference from original article:
- larger IMDB corpus, longer sentences; sentence length is very important, just like data size
- smaller embedding dimension, 20 instead of 300
- 2 filter sizes instead of original 3
- much fewer filters; experiments show that 3-10 is enough; original work uses 100
- random initialization is no worse than word2vec init on IMDB corpus
- sliding Max Pooling instead of original Global Pooling
Dependencies
- The Keras Deep Learning library and most recent Theano backend should be installed. You can use pip for that.
Not tested with TensorFlow, but should work.
