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

Zihan Zhang; Meng Fang; Ling Chen; Mohammad-Reza Namazi-Rad

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arXiv · 2204.09874

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

Zihan Zhang·Meng Fang·Ling Chen·Mohammad-Reza Namazi-Rad

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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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Zihan Zhang, Meng Fang, Ling Chen, Mohammad-Reza Namazi-Rad. 2022-04-21. Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics. https://arxiv.org/abs/2204.09874

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