This repository contains the code for ourl EMNLP 2018 paper "GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model", you can find here.
Some code are based on the pytorch implementation of AVITM: https://github.com/hyqneuron/pytorch-avitm The Topic Coherence Evaluation is from: https://github.com/jhlau/topic_interpretability Thanks for sharing code!
- python 3.6
- pytorch 0.4
- numpy
- python 2.7 for topic coherence evaluation
$ python pytorch_run.py --startIt may take some time to generate the biterms (it's too large to upload the pickle, so I upload the original files). It will generate the top 10 words in each topic after each epoch in 'topic_interpretability/data/topics_20news.txt', and you can use the code in the topic_interpretability folder:
$ ./run-oc.shIf you find the code helpful, please kindly cite the paper:
> @InProceedings{D18-1495,
author = "Zhu, Qile and Feng, Zheng and Li, Xiaolin",
title = "GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "4663--4672",
location = "Brussels, Belgium",
url = "http://aclweb.org/anthology/D18-1495"
}
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