Topic Memory Networks (TMN) employs a novel topic memory mechanism to encode latent topic representations indicative of class labels in short text classification. TMN jointly explores topic inference and text classification with memory networks in an end-to-end manner.
A sampled data is provided in data/tmn/tmn_data.txt from TagMyNews. One data sample per line, the text and the label are separated by ######.
text1######label1
text2######label2
text3######label3
...Preprocess data:
$ cd scripts/
$ python process_tmn.py <input_data_file>
e.g. python process_tmn.py ../data/tmn/tmn_data.txtRun TMN:
$ python tmn_run.py <input_data_dir> <embedding_file> <output_dir> <topic_num>
e.g. python tmn_run.py ../data/tmn /emb/glove.6B.200d.txt ../output 50More detailed configurations can be found in tmn_run.py.
@inproceedings{DBLP:conf/emnlp/Zeng18,
author = {Jichuan Zeng and
Jing Li and
Yan Song and
Cuiyun Gao and
Michael R. Lyu and
Irwin King},
title = "{Topic Memory Networks for Short Text Classification}",
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, {EMNLP} 2018, Brussels, Belgium, October 31–November 4, 2018},
year = {2018},
}The code is for research purpose only and released under the Apache License, Version 2.0 (https://www.apache.org/licenses/LICENSE-2.0).