https://github.com/gatenlp/cantm

https://github.com/gatenlp/cantm

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• Host: GitHub
• URL: https://github.com/gatenlp/cantm
• Owner: GateNLP
• Created: 2020-12-15T16:26:25.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2021-05-12T15:32:58.000Z (over 3 years ago)
• Last Synced: 2024-04-16T07:59:21.531Z (9 months ago)
• Language: Python
• Size: 2.46 MB
• Stars: 8
• Watchers: 12
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# CANTM

Implementation of Classification Aware Neural Topic Model (CANTM) and application for misinformation category classification

Please cite the following paper in your publication involving this work:

Xingyi Song, Johann Petrak, Ye Jiang, Iknoor Singh, Diana Maynard, Kalina Bontcheva, Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus, In arXiv:2006.03354, 2020.

## Install

* Requirement:

  * miniconda: https://docs.conda.io/en/latest/miniconda.html

* Install conda environment: conda env create -f environmentGPU.yml

* Active conda environment: conda activate wvmisinfoGPU

* Download NLTK and BERT models: python getPerpare.py

## Run Experiments:

### Covid Exp

* Get COVID data: 

```

cd wvCovidData

bash getCovidData.sh

```

* Run CANTM: bash runCANTM_covid.sh

* Run BERT: bash runBert_covid.sh

* Run NVDM: runNVDM_covid.sh

* Run NVDM_bert: runNVDM_bert_covid.sh

* Run LDA: runLDA_covid.sh

### Update topics with unlabelled data:

* Update classification-aware topic on COVID (note require `bash runCANTM_covid.sh' first), the latest unlabelled data not included please contact author. for the data: bash updateCovid.sh 

### Run Covid data on SCHOLAR:

* Perpare data for SCHOLAR: bash outputForScholar.sh 

* The Scholar ready data format (splited in 5 folds) will be in 'testScholarfold'

* Please follow the instruction in Scholar to run the experiment

* Patched version of 'run_scholar.py' in Scholar_patch/, that output f1 score. (Copy to original Scholar folder to use)