KATE: K-Competitive Autoencoder for Text

Code accompanying the paper "KATE: K-Competitive Autoencoder for Text"

Prerequisites

This code is written in python. To use it you will need:

• Python 2.7
• A recent version of Numpy
• A recent version of NLTK
• Tensorflow >= 1.0
• Keras >=2.0

Getting started

To preprocess the corpus, e.g., 20 Newsgroups, just run the following:

    python construct_20news.py -train [train_dir] -test [test_dir] -o [out_dir] -threshold [word_freq_threshold] -topn [top_n_words]

It outputs 4 json files under the [out_dir] directory: train_data, train_label, test_data and test_label.

To train the KATE model, just run the following:

    python train.py -i [train_data] -nd [num_topics] -ne [num_epochs] -bs [batch_size] -nv [num_validation] -ctype kcomp -ck [top_k] -sm [model_file]

To predict on the test set, just run the following:

    python pred.py -i [test_data] -lm [model_file] -o [output_doc_vec_file] -st [output_topics] -sw [output_sample_words] -wc [output_word_clouds]

Architecture

Experiment results on 20 Newsgroups

PCA on the 20-D document vectors

TSNE on the 20-D document vectors

Five nearest neighbors in the word representation space

Extracted topics

Reference

If you found this code useful, please cite the following paper:

Yu Chen and Mohammed J. Zaki. "KATE: K-Competitive Autoencoder for Text." In Proceedings of the ACM SIGKDD International Conference on Data Mining and Knowledge Discovery. Aug 2017.

@inproceedings {chen2017kate,
author = { Yu Chen and Mohammed J. Zaki },
title = { KATE: K-Competitive Autoencoder for Text },
booktitle = { Proceedings of the ACM SIGKDD International Conference on Data Mining and Knowledge Discovery },
doi = { http://dx.doi.org/10.1145/3097983.3098017 },
year = { 2017 },
month = { Aug }
}