This is a full (slightly modified and extended) TensorFlow implementation of the model presented by Kim in Convolutional Neural Networks for Sentence Classification.

With this code you can reproduce almost all results presented by Kim and the results we present in our Word Embeddings and Their Use In Sentence Classification Tasks paper.

If you're using this code please make sure you cite both following papers:

• Kim's paper:

@article{Kim2014ConvNetSent,
  Author = {Kim, Yoon},
  Journal = {arXiv preprint arXiv:1408.5882},
  Title = {Convolutional Neural Networks for Sentence Classification},
  Year = {2014}
}

• Our paper:

@article{Man2016WordEmbbed,
  Author = {Mandelbaum, Amit and Shalev, Adi},
  Journal = {arXiv preprint arXiv:1610.08229},
  Title = {Word Embeddings and Their Use In Sentence Classification Tasks},
  Year = {2016}
}

Also, small parts of the code were taken from:

• Yoon Kim's git repository
• Denny Britz's git repository
  We'd like to thank them for that.

• Supports Random, Static, and non-Static modes.
• Runs on (almost) all datasets from the original article + 2 from here + 2 new ones.
• Optimizer changed to ADAM, improving training time and some of the results.
• Support for L2 loss added.

• Python (2.7)
• NumPy
• TensorFlow (>=0.8)
• Pandas

1. Clone the repository recursively to get all folder and subfolders
2. Download Google's word embeddings binary file from https://code.google.com/p/word2vec/ extract it, and place it under data/ folder

1. Choose the dataset you want to run on by uncommenting it in the last section of sentence_convnet_final.py
2. Run python sentence_convnet_final.py --static <True/False> --random <True/False>
   Below is a list of modes and appropriate flags:

• Random: --static False --Random True
• Static: --static True --Random False
• Non-Static: --static False --Random False