Project Introduction:

This is the source code for our paper "When Graph Convolution Meets Double Attention: Online Privacy Disclosure Detection with Multi-Label Text Classification", accepted to Data Mining and Knowledge Discovery. In this project, we propose a new privacy disclosure detection model with multi-label text classification, which considers three different sources of relevant information: the input text itself, the label-to-text correlation, and the label-to-label correlation. The architecture of the proposed model is as follows:

Requirements:

• Python 3.7.10
• Pytorch 1.7.1
• Numpy 1.21.5
• Tqdm 4.61.2
• Nltk 3.6.2
• Gensim 3.8.3

Usage:

• Dataset:

  Download the dataset introduced in [1] and put it into "data" folder.

• hyper-parameter tuning:

  Use the training subset and validation subset to finish the hyper-parameter tuning. All the hyper-parameters are decided in this process. Run the code:

  python parameter_tuning.py

• Train the model:

  Train the model with the hyper-parameters tuned. Run the code:

  python classification.py

• Test the model: Rename the trained model as "model_best.pth" and put it into the "\attention\models_saved" folder. Then run the code:

  python testing.py

Reference:

• [1] Xuemeng Song et al. A personal privacy preserving framework: I let you know who can see what. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 295–304, 2018.