# ArticlePairMatching

**Repository Path**: nnroy/ArticlePairMatching

## Basic Information

- **Project Name**: ArticlePairMatching
- **Description**: The code of ACL 2019 paper: Matching Article Pairs with Graphical Decomposition and Convolutions
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-04-05
- **Last Updated**: 2021-04-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Doc2Graph
==============================
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.

I have uploaded the dataset to baidupan as well. You can download via:
link:https://pan.baidu.com/s/1EDna1rspOceQUENprgXZFg  password:nhzs

**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