Check our latest topic modeling toolkit TopMost !

Code for Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder

EMNLP2020 | Presentation Video

Usage

0. Prepare environment

requirements:

python==3.6
tensorflow-gpu==1.13.1
scipy==1.5.2
scikit-learn==0.23.2

1. Prepare data

Note: the data in the path ./data has been preprocessed with tokenization, filtering non-Latin characters, etc before.

python utils/preprocess.py --data_path data/stackoverflow --output_dir input/stackoverflow

2. Run the model

python run_NQTM.py --data_dir input/stackoverflow --output_dir output

3. Evaluation

topic coherence: coherence score.

topic diversity:

python utils/TU.py --data_path output/top_words_T15_K50_1th

Citation

If you want to use our code, please cite as

@inproceedings{wu2020short,
    title = "Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder",
    author = "Wu, Xiaobao  and
    Li, Chunping  and
    Zhu, Yan  and
    Miao, Yishu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.138",
    doi = "10.18653/v1/2020.emnlp-main.138",
    pages = "1772--1782"
}

BobXWu/NQTM