This repo provides functions for creating and analyzing word co-occurrence networks in Python and R. Examples are provided in the gow_toy scripts.
To get a feel for what word co-occurrence networks are, what they can be used for, and the impact of the different parameters, have a look at this interactive web app (GoWvis). You can paste your own text and download the graph edgelist.
In this repository, word co-occurrence networks are defined in the manner of Mihalcea and Tarau (2004). Some more recent papers using word co-occurrence networks can be found below.
ACL 2018:
@inproceedings{shang2018unsupervised,
title={Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization},
author={Shang, Guokan and Ding, Wensi and Zhang, Zekun and Tixier, Antoine Jean-Pierre and Meladianos, Polykarpos and Vazirgiannis, Michalis and Lorr{\'e}, Jean-Pierre},
booktitle={Proceedings of the 2018 Conference of the Association for Computational Linguistics},
year={2018}
}
EMNLP 2016:
@inproceedings{tixier2016graph,
title={A graph degeneracy-based approach to keyword extraction},
author={Tixier, Antoine and Malliaros, Fragkiskos and Vazirgiannis, Michalis},
booktitle={Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
pages={1860--1870},
year={2016}
}