Related papers: An Enhanced Model-based Approach for Short Text Clustering

An Enhanced Model-based Approach for Short Text Clustering

URL: http://arxiv.org/abs/2507.13793v1
Date: Fri, 18 Jul 2025 10:07:42 GMT
Title: An Enhanced Model-based Approach for Short Text Clustering
Authors: Enhao Cheng, Shoujia Zhang, Jianhua Yin, Xuemeng Song, Tian Gan, Liqiang Nie,
Abstract summary: Short text clustering has become increasingly important with the popularity of social media like Twitter, Google+, and Facebook.<n>Existing methods can be broadly categorized into two paradigms: topic model-based approaches and deep representation learning-based approaches.<n>We propose a collapsed Gibbs Sampling algorithm for the Dirichlet Multinomial Mixture model (GSDMM), which effectively handles the sparsity and high dimensionality of short texts.<n>Based on several aspects of GSDMM that warrant further refinement, we propose an improved approach, GSDMM+, designed to further optimize its performance.
Score: 58.60681789677676
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Short text clustering has become increasingly important with the popularity of social media like Twitter, Google+, and Facebook. Existing methods can be broadly categorized into two paradigms: topic model-based approaches and deep representation learning-based approaches. This task is inherently challenging due to the sparse, large-scale, and high-dimensional characteristics of the short text data. Furthermore, the computational intensity required by representation learning significantly increases the running time. To address these issues, we propose a collapsed Gibbs Sampling algorithm for the Dirichlet Multinomial Mixture model (GSDMM), which effectively handles the sparsity and high dimensionality of short texts while identifying representative words for each cluster. Based on several aspects of GSDMM that warrant further refinement, we propose an improved approach, GSDMM+, designed to further optimize its performance. GSDMM+ reduces initialization noise and adaptively adjusts word weights based on entropy, achieving fine-grained clustering that reveals more topic-related information. Additionally, strategic cluster merging is employed to refine clustering granularity, better aligning the predicted distribution with the true category distribution. We conduct extensive experiments, comparing our methods with both classical and state-of-the-art approaches. The experimental results demonstrate the efficiency and effectiveness of our methods. The source code for our model is publicly available at https://github.com/chehaoa/VEMC.

Related papers

An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets [0.0]
We present an improved clustering technique for large textual datasets by leveraging fine-tuned word embeddings.<n>We show significant improvements in clustering metrics such as silhouette score, purity, and adjusted rand index (ARI)<n>The proposed technique will help to bridge the gap between semantic understanding and statistical robustness for large-scale text-mining tasks.
arXiv Detail & Related papers (2025-02-22T08:28:41Z)
A Top-down Graph-based Tool for Modeling Classical Semantic Maps: A Crosslinguistic Case Study of Supplementary Adverbs [50.982315553104975]
Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms.<n>Most SMMs are manually built by human experts using bottom-up procedures.<n>We propose a novel graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner.
arXiv Detail & Related papers (2024-12-02T12:06:41Z)
Fast and Scalable Semi-Supervised Learning for Multi-View Subspace Clustering [13.638434337947302]
FSSMSC is a novel solution to the high computational complexity commonly found in existing approaches. The method generates a consensus anchor graph across all views, representing each data point as a sparse linear combination of chosen landmarks. The effectiveness and efficiency of FSSMSC are validated through extensive experiments on multiple benchmark datasets of varying scales.
arXiv Detail & Related papers (2024-08-11T06:54:00Z)
End-to-end Learnable Clustering for Intent Learning in Recommendation [54.157784572994316]
We propose a novel intent learning method termed underlineELCRec. It unifies behavior representation learning into an underlineEnd-to-end underlineLearnable underlineClustering framework. We deploy this method on the industrial recommendation system with 130 million page views and achieve promising results.
arXiv Detail & Related papers (2024-01-11T15:22:55Z)
Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search [60.626459715780605]
Given a descriptive text query, text-based person search aims to retrieve the best-matched target person from an image gallery. Such a cross-modal retrieval task is quite challenging due to significant modality gap, fine-grained differences and insufficiency of annotated data. In this paper, we propose a simple yet effective dual Transformer model for text-based person search.
arXiv Detail & Related papers (2023-11-15T16:26:49Z)
Large-scale Fully-Unsupervised Re-Identification [78.47108158030213]
We propose two strategies to learn from large-scale unlabeled data. The first strategy performs a local neighborhood sampling to reduce the dataset size in each without violating neighborhood relationships. A second strategy leverages a novel Re-Ranking technique, which has a lower time upper bound complexity and reduces the memory complexity from O(n2) to O(kn) with k n.
arXiv Detail & Related papers (2023-07-26T16:19:19Z)
A distribution-free mixed-integer optimization approach to hierarchical modelling of clustered and longitudinal data [0.0]
We introduce an innovative algorithm that evaluates cluster effects for new data points, thereby increasing the robustness and precision of this model. The inferential and predictive efficacy of this approach is further illustrated through its application in student scoring and protein expression.
arXiv Detail & Related papers (2023-02-06T23:34:51Z)
Tight integration of neural- and clustering-based diarization through deep unfolding of infinite Gaussian mixture model [84.57667267657382]
This paper introduces a it trainable clustering algorithm into the integration framework. Speaker embeddings are optimized during training such that it better fits iGMM clustering. Experimental results show that the proposed approach outperforms the conventional approach in terms of diarization error rate.
arXiv Detail & Related papers (2022-02-14T07:45:21Z)
Meeting Summarization with Pre-training and Clustering Methods [6.47783315109491]
HMNetcitehmnet is a hierarchical network that employs both a word-level transformer and a turn-level transformer, as the baseline. We extend the locate-then-summarize approach of QMSumciteqmsum with an intermediate clustering step. We compare the performance of our baseline models with BART, a state-of-the-art language model that is effective for summarization.
arXiv Detail & Related papers (2021-11-16T03:14:40Z)
Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing [113.52575069030192]
Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.
arXiv Detail & Related papers (2020-10-02T10:41:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.