CNN-Sentence-Classifier

Simplified implementation of "Convolutional Neural Networks for Sentence Classification" paper

Usage

Install Keras
Repository contains "Movie reviews with one sentence per review" (Pang and Lee, 2005) dataset in sample_dataset.
Alternatively, to use some other dataset, make two files
- input.txt where each line is a sentence to be classified
- label.txt where each line is the label for corresponding line in input.txt
Make model folder by running mkdir model
Refer this to train or download Glove embeddings and this for Word2Vec embeddings.
Run python3 app/train.py --data_dir=path_to_folder_containing_input.txt_and_label.txt --embedding_file_path=path_to_embedding_vectors_file --model_name=name_of_model_from_the_paper
For example, if data is in data folder, embedding file is vectors.txt and model is cnn_static, run python3 app/train.py --data_dir=data --embedding_file_path=vectors.txt --model_name=cnn_static
To define your own model, pass model_name as self, define your model in app/model/model.py and invoke from model_selector function (in model.py).
All supported arguments can be seen in here

Kim, Yoon. "Convolutional neural networks for sentence classification." arXiv preprint arXiv:1408.5882 (2014)
Summary of paper