Papers with cluster-level representation learning
Improving Deep Embedded Clustering via Learning Cluster-level Representations (2022.coling-1)
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| Challenge: | Existing efforts to learn meaningful representations at the instance level are limited. |
| Approach: | They propose a deep embedded clustering model with cluster-level representation learning to jointly learn cluster and instance level representations. |
| Outcome: | The proposed model produces meaningful clusters on real-world short text datasets. |
HS-GC: Holistic Semantic Embedding and Global Contrast for Effective Text Clustering (2024.lrec-main)
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| Challenge: | In this paper, we introduce Holistic Semantic Embedding and Global Contrast (HS-GC) to learn the instance- and cluster-level representations. |
| Approach: | They propose a novel loss function that exploits different layers of semantic information in a deep neural network to provide a more holistic semantic text representation. |
| Outcome: | The proposed model outperforms the state-of-the-art model on five text datasets and improves clustering accuracy of 5.9% and 3.2% on the StackOverflow and TREC datasets. |