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

• URL: http://arxiv.org/abs/2204.09874v1
• Date: Thu, 21 Apr 2022 04:26:51 GMT
• Title: Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics
• Authors: Zihan Zhang, Meng Fang, Ling Chen, Mohammad-Reza Namazi-Rad
• Abstract summary: Recent work incorporates pre-trained word embeddings into Neural Topic Models (NTMs) 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.
• Score: 28.13990734234436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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