{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/reliable-pseudo-labeling-via-optimal","title":"Reliable Pseudo-labeling via Optimal Transport with Attention for Short Text Clustering","arxiv_id":"2501.15194","date":"2025-01-25","proceeding":null,"authors":["Zhihao Yao","Jixuan Yin","Bo Li"],"abstract":"Short text clustering has gained significant attention in the data mining community. However, the limited valuable information contained in short texts often leads to low-discriminative representations, increasing the difficulty of clustering. This paper proposes a novel short text clustering framework, called Reliable \\textbf{P}seudo-labeling via \\textbf{O}ptimal \\textbf{T}ransport with \\textbf{A}ttention for Short Text Clustering (\\textbf{POTA}), that generate reliable pseudo-labels to aid discriminative representation learning for clustering. Specially, \\textbf{POTA} first implements an instance-level attention mechanism to capture the semantic relationships among samples, which are then incorporated as a semantic consistency regularization term into an optimal transport problem. By solving this OT problem, we can yield reliable pseudo-labels that simultaneously account for sample-to-sample semantic consistency and sample-to-cluster global structure information. Additionally, the proposed OT can adaptively estimate cluster distributions, making \\textbf{POTA} well-suited for varying degrees of imbalanced datasets. Then, we utilize the pseudo-labels to guide contrastive learning to generate discriminative representations and achieve efficient clustering. Extensive experiments demonstrate \\textbf{POTA} outperforms state-of-the-art methods. The code is available at: \\href{https://github.com/YZH0905/POTA-STC/tree/main}{https://github.com/YZH0905/POTA-STC/tree/main}.","url_abs":"https://arxiv.org/abs/2501.15194v3","url_pdf":"https://arxiv.org/pdf/2501.15194v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"reliable-pseudo-labeling-via-optimal","repo_url":"https://github.com/yzh0905/pota-stc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"short-text-clustering","task_name":"Short Text Clustering"},{"task_slug":"text-clustering","task_name":"Text Clustering"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}