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The code of ACL 2019 paper: Matching Article Pairs with Graphical Decomposition and Convolutions

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We put the code for constructing Concept Interaction Graph and run experiments in our ACL 2019 submission here.

We also put the datasets here: Chinese News Same Event dataset (CNSE) and Chinese News Same Story dataset (CNSS).

Requirement

To run the code successfully, you will need (just install the most recent version)

  • Pytorch
  • Graph-tool

How to use

**Run experiments: ** Please go to src/models/CCIG, and run script.sh.

**Process data: ** Please go to src/models/CCIG/data/ and run feature_extractor.py.

Datasets: The CNSE dataset in the paper is in data/raw/event-story-cluster/same_event_doc_pair.txt, and the CNSS dataset is located in data/raw/event-story-cluster/same_story_doc_pair.txt.

**CIG visualization: ** we put some figures of CIG with community detection in the folder ``CIG visualization by graph-tool''.

Project Organization

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data
│
├── src                <- Source code for use in this .
   │
   └──models/CCIG         
       │
       ├── data     <- code for extract graph features 
       ├── util     <- code for some functions 
       ├── loader.py  <- code for loading graph features
       ├── main.py  <- main code for running models
       └── models   <- pytorch code for our model

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The code of ACL 2019 paper: Matching Article Pairs with Graphical Decomposition and Convolutions

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