Check our latest topic modeling toolkit TopMost !

Code for Discovering Topics in Long-tailed Corpora with Causal Intervention

ACL2021 Findings

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

Download preprocessed datasets from Google Drive and extract files to the path ./data.

2. Run the model

python main.py --data_dir ./data/{dataset} --output_dir ./output

3. Evaluation

topic coherence: coherence score.

topic diversity:

python utils/TU.py --data_path {path of topic word file}

Citation

If you are interested in our work, please cite as

@inproceedings{wu2021discovering,
    title = "Discovering Topics in Long-tailed Corpora with Causal Intervention",
    author = "Wu, Xiaobao  and
    Li, Chunping  and
    Miao, Yishu",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.15",
    doi = "10.18653/v1/2021.findings-acl.15",
    pages = "175--185",
}

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EMNLP2020 Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder

NLPCC2020 Learning Multilingual Topics with Neural Variational Inference