检索结果-内蒙古大学图书馆

Toward deep multi-view document clustering using enhanced semantic embedding and consistent context semantics

KNOWLEDGE AND INFORMATION SYSTEMS 2025年第2期67卷 1073-1100页

作者： Du, Yongsheng Sun, Hongwei Abdollahi, Mohammadjavad Jining Univ Coll Comp Sci & Engn Qufu 273155 Shandong Peoples R China Univ Lian Bushehr Dept Comp Engn Bushehr Iran

multi-view document clustering (MVDC) is a sophisticated approach in natural language processing that leverages multiple representations or views of data to improve clustering performance. Existing solutions are challenging due to inconsistency of document views, high dimensions, and sparseness in text documents. On the other hand, existing MVDC-based methods often depend on the performance of bag-of-words and pretrained language models. However, these models usually do not consider contextual semantics and are suitable for single-view document clustering. This paper addresses these challenges by proposing a deep MVDC model that utilizes enhanced semantic embedding and consistent context semantics (SECS). SECS uses semantic embedding to address high-dimensional challenges by considering complementary semantic information. Meanwhile, SECS takes advantage of the potential benefits of view-consistent context semantics based on pretrained language models. The proposed model captures intricate semantic relationships between words and documents through advanced embedding techniques, ensuring a richer and more nuanced representation of textual content. Furthermore, by incorporating consistent context semantics, SECS maintains contextual integrity across multiple views, leading to more coherent and meaningful clusters. Experimental results on benchmark datasets demonstrate the superiority of our model over state-of-the-art MVDC methods, highlighting its effectiveness in improving clustering quality and interpretability.

关键词： multi-view document clustering Semantic embedding Context semantics Pretrained language model

来源：评论

学校读者我要写书评

暂无评论

multi-view document clustering with Joint Contrastive Learning 1

引用

11th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC)

作者： Bai, Ruina Huang, Ruizhang Qin, Yongbin Chen, Yanping Guizhou Univ Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China

ISBN: (数字)9783031171208

ISBN: (纸本)9783031171208;9783031171192

multi-view document clustering, which learns common representations from multiple views to achieve consistent partition, has emerged lots of increasing work. Though promising performance has been demonstrated in various applications, their view representations are learned with no consideration of achieving a consistent clustering partition. In this paper, we propose a multi-view document clustering model with Joint Contrastive learning (MCJC) to address the aforementioned issue. Our model learns the view representations with a joint contrastive learning module by introducing a task-specific objective so that it can effectively achieve consistency both in cluster-wise and featurewise hidden spaces. Meanwhile, in the clustering module, we collect the view-level cluster agreement and document-level clustering partition to refine the contrastive learning and obtain document assignments. As a result, the proposed model can use a joint contrastive module to learn clustering-friendly representation and through multi-level clustering to achieve better clustering performance. Extensive experiments on real datasets demonstrate that our model achieves state-of-the-art clustering effectiveness.

关键词： multi-view document clustering Contrastive learning Deep clustering

来源：评论

学校读者我要写书评

暂无评论

CSMDC: Exploring consistently context semantics for multi-view document clustering

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 261卷

作者： Bai, Ruina Huang, Ruizhang Xu, Le Qin, Yongbin Guizhou Univ Coll Comp Sci & Technol Text Comp & Cognit Intelligence Engn Res Ctr Natl State Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China

multi-view document clustering, which aims to discover clustering partitions based on multiple document views, has attracted increasing research interest. However, the potential advantages of incorporating context semantics to enhance multi-view document clustering are yet to be fully explored. To address the above limitation, we propose a deep multi-view document clustering model that explores consistent context semantics called CSMDC which consists of three modules. Specifically, a novel view-translator is designed to convert non-contextual document views into contextual views. With its help, all document views can be processed to obtain their semantic representations within the view-translator representation learning module. Then the data-based view consistency self-supervising module is developed to fine-tune the semantic representations of document views by jointly incorporating view-wise representation relevance and consistent clustering assignments. Additionally, the task-based document clustering module is employed to simultaneously improve the view semantic representations and document clustering results. To the best of our knowledge, this is the first study to explicitly apply consistent context semantics under the guidance of data-based and task- based objectives in multi-view document clustering. Comprehensive experimental results demonstrated the effectiveness of the proposed model.

关键词： multi-view document clustering view-translator Context semantics view consistency self-supervising

来源：评论

学校读者我要写书评

暂无评论

A hierarchical consensus learning model for deep multi-view document clustering

引用

INFORMATION FUSION 2024年 111卷

作者： Bai, Ruina Huang, Ruizhang Chen, Yanping Qin, Yongbin Xu, Yong Zheng, Qinghua Guizhou Univ Coll Comp Sci & Technol Text Comp & Cognit Intelligence Engn Res Ctr State Key Lab Publ Big DataNatl Educ Minist Guiyang Guizhou Peoples R China Harbin Inst Technol Biocomp Res Ctr Shenzhen Guangdong Peoples R China Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian Shaanxi Peoples R China

document clustering, a fundamental task in natural language processing, aims to divede large collections of documents into meaningful groups based on their similarities. multi -view document clustering (MvDC) has emerged as a promising approach, leveraging information from diverse views to improve clustering accuracy and robustness. However, existing multi -view clustering methods suffer from two issues: (1) a lack of interrelations across documents during consensus semantic learning;(2) the neglect of consensus structure mining in the multi -view document clustering. To address these issues, we propose a Hierarchical Consensus Learning model for multi -view document clustering, termed as MvDC-HCL. Our model incorporates two key modules: The Data -oriented Consensus Semantic Learning (CSeL) module focuses on learning consensus semantics across various views by leveraging a hybrid contrastive consensus objective. The Task -oriented Consensus Structure clustering (CStC) module employs a gated fusion network and clustering -driven structure contrastive learning to mine consensus structures effectively. Specifically, CSeL module constructs a contrastive consensus learning objective based on intra-sample and inter -sample relationships in multi -view data, aiming to optimize the view semantic representations obtained by the semantic learner. This facilitates consistent semantic learning across various views of the same sample and consistent relationship learning among samples from different views. Then, the learned view semantic representations are fed into the fusion network of CStC to obtain fused sample semantic representations. Together with the view semantic representations, sample -level and view -level clustering structures are derived for consensus structure mining. Additionally, CStC introduces clustering -driven objectives to guide consensus structure mining and achieve consistent clustering results. By hierarchically extracting implicit consensus semantics and

关键词： Hierarchical consensus learning multi-view document clustering Consensus semantic Consensus structure

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：