Discriminative Representation learning via Attention-Enhanced Contrastive Learning for Short Text Clustering
- URL: http://arxiv.org/abs/2501.03584v3
- Date: Sun, 26 Jan 2025 05:51:00 GMT
- Title: Discriminative Representation learning via Attention-Enhanced Contrastive Learning for Short Text Clustering
- Authors: Zhihao Yao,
- Abstract summary: We propose a novel short text clustering method, called Discriminative Representation learning via textbfAttention-textbfEnhanced textbfContrastive textbfL.
Experimental results demonstrate that the proposed textbfAECL outperforms state-of-the-art methods.
- Score: 1.6788443047694643
- License:
- Abstract: Contrastive learning has gained significant attention in short text clustering, yet it has an inherent drawback of mistakenly identifying samples from the same category as negatives and then separating them in the feature space (false negative separation), which hinders the generation of superior representations. To generate more discriminative representations for efficient clustering, we propose a novel short text clustering method, called Discriminative Representation learning via \textbf{A}ttention-\textbf{E}nhanced \textbf{C}ontrastive \textbf{L}earning for Short Text Clustering (\textbf{AECL}). The \textbf{AECL} consists of two modules which are the pseudo-label generation module and the contrastive learning module. Both modules build a sample-level attention mechanism to capture similarity relationships between samples and aggregate cross-sample features to generate consistent representations. Then, the former module uses the more discriminative consistent representation to produce reliable supervision information for assist clustering, while the latter module explores similarity relationships and consistent representations optimize the construction of positive samples to perform similarity-guided contrastive learning, effectively addressing the false negative separation issue. Experimental results demonstrate that the proposed \textbf{AECL} outperforms state-of-the-art methods. If the paper is accepted, we will open-source the code.
Related papers
- Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment [69.67015515485349]
We propose AutoRegEmbed, a contrastive learning method built on embedding conditional probability distributions.
We show that our method significantly outperforms traditional contrastive learning approaches.
arXiv Detail & Related papers (2025-02-17T03:36:25Z) - Reliable Pseudo-labeling via Optimal Transport with Attention for Short Text Clustering [6.182375768528008]
This paper proposes a novel short text clustering framework, called Reliable textbfPseudo-labeling via textbfOptimal textbfTransport.
textbfPOTA generates reliable pseudo-labels to aid discriminative representation learning for clustering.
arXiv Detail & Related papers (2025-01-25T12:13:38Z) - Contrastive Learning Subspace for Text Clustering [4.065026352441705]
We propose a novel text clustering approach called Subspace Contrastive Learning (SCL)
The proposed SCL consists of two main modules: (1) a self-expressive module that constructs virtual positive samples and (2) a contrastive learning module that further learns a discriminative subspace to capture task-specific cluster-wise relationships among texts.
Experimental results show that the proposed SCL method not only has achieved superior results on multiple task clustering datasets but also has less complexity in positive sample construction.
arXiv Detail & Related papers (2024-08-26T09:08:26Z) - RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations [27.775731666470175]
New Intent Discovery (NID) aims to identify novel intent groups in the open-world scenario.
Current methods face issues with inaccurate pseudo-labels and poor representation learning.
We propose a Robust New Intent Discovery framework optimized by an EM-style method.
arXiv Detail & Related papers (2024-04-13T11:58:28Z) - Deep Contrastive Multi-view Clustering under Semantic Feature Guidance [8.055452424643562]
We propose a multi-view clustering framework named Deep Contrastive Multi-view Clustering under Semantic feature guidance (DCMCS)
By minimizing instance-level contrastive loss weighted by semantic similarity, DCMCS adaptively weakens contrastive leaning between false negative pairs.
Experimental results on several public datasets demonstrate the proposed framework outperforms the state-of-the-art methods.
arXiv Detail & Related papers (2024-03-09T02:33:38Z) - Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice.
We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
arXiv Detail & Related papers (2023-12-27T09:03:43Z) - Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic
Segmentation [59.37587762543934]
This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS)
Existing methods suffer from a granularity inconsistency regarding the usage of group tokens.
We propose the prototypical guidance network (PGSeg) that incorporates multi-modal regularization.
arXiv Detail & Related papers (2023-10-29T13:18:00Z) - BERM: Training the Balanced and Extractable Representation for Matching
to Improve Generalization Ability of Dense Retrieval [54.66399120084227]
We propose a novel method to improve the generalization of dense retrieval via capturing matching signal called BERM.
Dense retrieval has shown promise in the first-stage retrieval process when trained on in-domain labeled datasets.
arXiv Detail & Related papers (2023-05-18T15:43:09Z) - Exploring Non-Contrastive Representation Learning for Deep Clustering [23.546602131801205]
Non-contrastive representation learning for deep clustering, termed NCC, is based on BYOL, a representative method without negative examples.
NCC forms an embedding space where all clusters are well-separated and within-cluster examples are compact.
Experimental results on several clustering benchmark datasets including ImageNet-1K demonstrate that NCC outperforms the state-of-the-art methods by a significant margin.
arXiv Detail & Related papers (2021-11-23T12:21:53Z) - Neighborhood Contrastive Learning for Novel Class Discovery [79.14767688903028]
We build a new framework, named Neighborhood Contrastive Learning, to learn discriminative representations that are important to clustering performance.
We experimentally demonstrate that these two ingredients significantly contribute to clustering performance and lead our model to outperform state-of-the-art methods by a large margin.
arXiv Detail & Related papers (2021-06-20T17:34:55Z) - Graph Contrastive Clustering [131.67881457114316]
We propose a novel graph contrastive learning framework, which is then applied to the clustering task and we come up with the Graph Constrastive Clustering(GCC) method.
Specifically, on the one hand, the graph Laplacian based contrastive loss is proposed to learn more discriminative and clustering-friendly features.
On the other hand, a novel graph-based contrastive learning strategy is proposed to learn more compact clustering assignments.
arXiv Detail & Related papers (2021-04-03T15:32:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.