@inproceedings{shahid-etal-2023-hyhtm,
title = "{H}y{HTM}: Hyperbolic Geometry-based Hierarchical Topic Model",
author = "Shahid, Simra and
Anand, Tanay and
Srikanth, Nikitha and
Bhatia, Sumit and
Krishnamurthy, Balaji and
Puri, Nikaash",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.742",
doi = "10.18653/v1/2023.findings-acl.742",
pages = "11672--11688",
abstract = "Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies in a collection of documents. However, traditional HTMs often produce hierarchies where lower-level topics are unrelated and not specific enough to their higher-level topics. Additionally, these methods can be computationally expensive. We present HyHTM - a Hyperbolic geometry-based Hierarchical Topic Model - that addresses these limitations by incorporating hierarchical information from hyperbolic geometry to explicitly model hierarchies in topic models. Experimental results with four baselines show that HyHTM can better attend to parent-child relationships among topics. HyHTM produces coherent topic hierarchies that specialize in granularity from generic higher-level topics to specific lower-level topics. Further, our model is significantly faster and leaves a much smaller memory footprint than our best-performing baseline. We have made the source code for our algorithm publicly accessible.",
}
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<abstract>Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies in a collection of documents. However, traditional HTMs often produce hierarchies where lower-level topics are unrelated and not specific enough to their higher-level topics. Additionally, these methods can be computationally expensive. We present HyHTM - a Hyperbolic geometry-based Hierarchical Topic Model - that addresses these limitations by incorporating hierarchical information from hyperbolic geometry to explicitly model hierarchies in topic models. Experimental results with four baselines show that HyHTM can better attend to parent-child relationships among topics. HyHTM produces coherent topic hierarchies that specialize in granularity from generic higher-level topics to specific lower-level topics. Further, our model is significantly faster and leaves a much smaller memory footprint than our best-performing baseline. We have made the source code for our algorithm publicly accessible.</abstract>
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%0 Conference Proceedings
%T HyHTM: Hyperbolic Geometry-based Hierarchical Topic Model
%A Shahid, Simra
%A Anand, Tanay
%A Srikanth, Nikitha
%A Bhatia, Sumit
%A Krishnamurthy, Balaji
%A Puri, Nikaash
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F shahid-etal-2023-hyhtm
%X Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies in a collection of documents. However, traditional HTMs often produce hierarchies where lower-level topics are unrelated and not specific enough to their higher-level topics. Additionally, these methods can be computationally expensive. We present HyHTM - a Hyperbolic geometry-based Hierarchical Topic Model - that addresses these limitations by incorporating hierarchical information from hyperbolic geometry to explicitly model hierarchies in topic models. Experimental results with four baselines show that HyHTM can better attend to parent-child relationships among topics. HyHTM produces coherent topic hierarchies that specialize in granularity from generic higher-level topics to specific lower-level topics. Further, our model is significantly faster and leaves a much smaller memory footprint than our best-performing baseline. We have made the source code for our algorithm publicly accessible.
%R 10.18653/v1/2023.findings-acl.742
%U https://aclanthology.org/2023.findings-acl.742
%U https://doi.org/10.18653/v1/2023.findings-acl.742
%P 11672-11688
Markdown (Informal)
[HyHTM: Hyperbolic Geometry-based Hierarchical Topic Model](https://aclanthology.org/2023.findings-acl.742) (Shahid et al., Findings 2023)
ACL
- Simra Shahid, Tanay Anand, Nikitha Srikanth, Sumit Bhatia, Balaji Krishnamurthy, and Nikaash Puri. 2023. HyHTM: Hyperbolic Geometry-based Hierarchical Topic Model. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11672–11688, Toronto, Canada. Association for Computational Linguistics.