Papers with Topic modelling
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling (2025.naacl-long)
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Yanan Ma, Chenghao Xiao, Chenhan Yuan, Sabine N Van Der Veer, Lamiece Hassan, Chenghua Lin, Goran Nenadic
| Challenge: | Existing topic modelling methods encode contextual information of documents while ignoring contextual details of candidate centroid words. Existing methods are limited by the contextualization gap. |
| Approach: | They propose a topic modelling method that builds upon candidate centroid word embeddings contextualized on the dataset and a self-similarity-based method to filter out less meaningful tokens. |
| Outcome: | The proposed method significantly enhances the coherence and diversity of generated topics, and handles noisy data, outperforming strong baselines. |
Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling (2024.lrec-main)
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| Challenge: | Topic modelling has found extensive use in automatically detecting significant topics within a corpus of documents, but there are certain drawbacks. |
| Approach: | They propose a framework that prompts large language models to generate topics from a given set of documents and establish evaluation protocols to assess the clustering efficacy of LLMs. |
| Outcome: | The proposed model generates relevant topic titles and adheres to human guidelines to refine and merge topics. |