Papers with short text clustering
An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering (2020.acl-main)
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| Challenge: | Existing approaches to cluster short text streams exploit short text in a batch way, but determine optimal batch size is difficult since we have no priori knowledge when the topics evolve. |
| Approach: | They propose an online Semantic-enhanced Dirichlet Model for short sext stream clustering which integrates the word-occurance semantic information into a new graphical model and clusters each arriving short text automatically in an online way. |
| Outcome: | The proposed model has better performance than state-of-the-art models on synthetic and real-world data sets. |
EASE: Entity-Aware Contrastive Learning of Sentence Embedding (2022.naacl-main)
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| Challenge: | Existing methods for learning sentence embeddings are fine-tuning general-purpose pretrained models with a particular training supervision. |
| Approach: | They propose a method for learning sentence embeddings via contrastive learning between sentences and related entities. |
| Outcome: | The proposed method outperforms baseline methods in multilingual settings on a variety of tasks. |
Supporting Clustering with Contrastive Learning (2021.naacl-main)
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Dejiao Zhang, Feng Nan, Xiaokai Wei, Shang-Wen Li, Henghui Zhu, Kathleen McKeown, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang
| Challenge: | Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space, but different categories overlap with each other at the beginning of the learning process. |
| Approach: | They propose a framework to leverage contrastive learning to promote better separation between different categories by optimizing a clustering objective defined in the representation space. |
| Outcome: | The proposed framework improves state-of-the-art accuracy and normalized mutual information on short text clustering and combines top-down and bottom-up instance discrimination to achieve better distances. |
MAST: A Multi-View Alignment Strategy for Optimal Transport-Based Contrastive Clustering of Short Text (2026.findings-acl)
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| Challenge: | Short text clustering has gained significant prominence due to its ubiquity in real-world applications. |
| Approach: | They propose a multi-view alignment strategy with transport-based clustering that integrates structural views to capture multi-granularity semantic features. |
| Outcome: | Experiments show that MAST outperforms state-of-the-art methods on benchmark datasets. |