Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics

Zhang, Z; Fang, M; Chen, L; Namazi-Rad, MR

Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics

Zhang, Z Fang, M Chen, L

Namazi-Rad, MR

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Publication Type:: Conference Proceeding
Citation:: NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 2022, pp. 3886-3893
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Z
dc.contributor.author	Fang, M
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729
dc.contributor.author	Namazi-Rad, MR
dc.date.accessioned	2023-03-23T05:16:20Z
dc.date.available	2023-03-23T05:16:20Z
dc.date.issued	2022-01-01
dc.identifier.citation	NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 2022, pp. 3886-3893
dc.identifier.isbn	9781955917711
dc.identifier.uri	http://hdl.handle.net/10453/168219
dc.description.abstract	Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality contextualized document representations, do we really need sophisticated neural models to obtain coherent and interpretable topics? In this paper, we conduct thorough experiments showing that directly clustering high-quality sentence embeddings with an appropriate word selecting method can generate more coherent and diverse topics than NTMs, achieving also higher efficiency and simplicity.
dc.language	en
dc.relation	TPG Telecom Limited
dc.relation.ispartof	NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-23T05:16:19Z
pubs.publication-status	Published

Abstract:

Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality contextualized document representations, do we really need sophisticated neural models to obtain coherent and interpretable topics? In this paper, we conduct thorough experiments showing that directly clustering high-quality sentence embeddings with an appropriate word selecting method can generate more coherent and diverse topics than NTMs, achieving also higher efficiency and simplicity.

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http://hdl.handle.net/10453/168219