GINopic is a framework for Graph Isomorphism Network (GIN)-based topic modeling. It leverages document similarity graphs to improve word correlations within topic modeling.

Datasets

We have used the following datasets:

Name	Source	# Docs	# Words	# Labels
20NewsGroups	20Newsgroups	16309	1612	20
BBC_News	BBC-News	2225	2949	5
SearchSnippets	SearchSnippets	12270	2000	8
Bio	Bio	18686	2000	20
StackOverflow	StackOverflow	15696	1860	20

How to cite this work?

This work has been accepted at NAACL 2024!

Read the paper in :

1. ACL Anthology
2. ArXiv

@inproceedings{adhya2024ginopic,
   title = "{GIN}opic: Topic Modeling with Graph Isomorphism Network",
   author = "Adhya, Suman  and
     Sanyal, Debarshi",
   editor = "Duh, Kevin  and
     Gomez, Helena  and
     Bethard, Steven",
   booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
   month = jun,
   year = "2024",
   address = "Mexico City, Mexico",
   publisher = "Association for Computational Linguistics",
   url = "https://aclanthology.org/2024.naacl-long.342",
   pages = "6171--6183",
   abstract = "Topic modeling is a widely used approach for analyzing and exploring large document collections. Recent research efforts have incorporated pre-trained contextualized language models, such as BERT embeddings, into topic modeling. However, they often neglect the intrinsic informational value conveyed by mutual dependencies between words. In this study, we introduce GINopic, a topic modeling framework based on graph isomorphism networks to capture the correlation between words. By conducting intrinsic (quantitative as well as qualitative) and extrinsic evaluations on diverse benchmark datasets, we demonstrate the effectiveness of GINopic compared to existing topic models and highlight its potential for advancing topic modeling.",

}

Acknowledgment

All experiments are conducted using OCTIS which is an integrated framework for topic modeling for comparing and optimizing topic models.

OCTIS: Silvia Terragni, Elisabetta Fersini, Bruno Giovanni Galuzzi, Pietro Tropeano, and Antonio Candelieri. (2021). OCTIS: Comparing and Optimizing Topic models is Simple!. EACL. https://www.aclweb.org/anthology/2021.eacl-demos.31/