Papers Text Clustering
“Text Clustering” 태그가 달린 논문 134편 · 필터 해제
CLUBench: A Clustering Benchmark
Clustering is a fundamental problem in data science with a long-standing research history, yielding numerous insightful algorithms. Despite this progress, a systematic and large-scale empirical evaluation that jointly co…
Text ClusteringDeep ClusteringTextClusterLab: An Integrated Framework for Reliable Text Clustering Studies
In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems. However, evaluating text clustering algorithms remains challenging s…
Recommendation SystemsIntent DiscoveryText ClusteringAdaGraph: A Graph-Native Clustering Algorithm That Overcomes the Curse of Dimensionality and Enables Scientific Discovery
We present AdaGraph, a graph-native clustering algorithm born from the Structure-Centric Machine Learning (SC-ML) paradigm -- a new field of unsupervised learning that replaces geometry-centric (distance-based) computati…
Dimensionality ReductionText ClusteringTopeax -- An Improved Clustering Topic Model with Density Peak Detection and Lexical-Semantic Term Importance
Text clustering is today the most popular paradigm for topic modelling, both in academia and industry. Despite clustering topic models' apparent success, we identify a number of issues in Top2Vec and BERTopic, which rema…
Text ClusteringTopic ModelsOptimized Algorithms for Text Clustering with LLM-Generated Constraints
Clustering is a fundamental tool that has garnered significant interest across a wide range of applications including text analysis. To improve clustering accuracy, many researchers have incorporated background knowledge…
Text ClusteringA Large-Language-Model Framework for Automated Humanitarian Situation Reporting
Timely and accurate situational reports are essential for humanitarian decision-making, yet current workflows remain largely manual, resource intensive, and inconsistent. We present a fully automated framework that uses …
Question GenerationText ClusteringClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation
Text clustering is a fundamental task in natural language processing, yet traditional clustering algorithms with pre-trained embeddings often struggle in domain-specific contexts without costly fine-tuning. Large languag…
Text ClusteringLLM-MemCluster: Empowering Large Language Models with Dynamic Memory for Text Clustering
Large Language Models (LLMs) are reshaping unsupervised learning by offering an unprecedented ability to perform text clustering based on their deep semantic understanding. However, their direct application is fundamenta…
Text ClusteringSDEC: Semantic Deep Embedded Clustering
The high dimensional and semantically complex nature of textual Big data presents significant challenges for text clustering, which frequently lead to suboptimal groupings when using conventional techniques like k-means …
Text ClusteringLMAR: Language Model Augmented Retriever for Domain-specific Knowledge Indexing
Retrieval Augmented Generation (RAG) systems often struggle with domain-specific knowledge due to performance deterioration of pre-trained embeddings and prohibitive computational costs of large language model (LLM)-base…
Data AugmentationText ClusteringReal-time News Story Identification
To improve the reading experience, many news sites organize news into topical collections, called stories. In this work, we present an approach for implementing real-time story identification for a news monitoring system…
Text ClusteringCSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass
As a fundamental task in Information Retrieval and Computational Linguistics, sentence representation has profound implications for a wide range of practical applications such as text clustering, content analysis, questi…
Contrastive LearningInformation RetrievalQuestion AnsweringRepresentation Learning+2Moving Past Single Metrics: Exploring Short-Text Clustering Across Multiple Resolutions
Cluster number is typically a parameter selected at the outset in clustering problems, and while impactful, the choice can often be difficult to justify. Inspired by bioinformatics, this study examines how the nature of …
ClusteringInformativenessShort Text ClusteringText ClusteringAdvanced Text Analytics -- Graph Neural Network for Fake News Detection in Social Media
Traditional Graph Neural Network (GNN) approaches for fake news detection (FND) often depend on auxiliary, non-textual data such as user interaction histories or content dissemination patterns. However, these data source…
ClusteringFake News DetectionGraph Neural NetworkText Clusteringk-LLMmeans: Scalable, Stable, and Interpretable Text Clustering via LLM-based Centroids
We introduce k-LLMmeans, a novel modification of the k-means algorithm for text clustering that leverages LLM-generated summaries as cluster centroids, capturing semantic nuances often missed by purely numerical averages…
ClusteringText ClusteringReliable Pseudo-labeling via Optimal Transport with Attention for Short Text Clustering
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 di…
ClusteringContrastive LearningRepresentation LearningShort Text Clustering+1Discriminative Representation learning via Attention-Enhanced Contrastive Learning for Short Text Clustering
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 featur…
ClusteringContrastive LearningPseudo LabelRepresentation Learning+2LITA: An Efficient LLM-assisted Iterative Topic Augmentation Framework
Topic modeling is widely used for uncovering thematic structures within text corpora, yet traditional models often struggle with specificity and coherence in domain-focused applications. Guided approaches, such as Seeded…
ClusteringSpecificityText ClusteringDial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues
Discovering customer intentions in dialogue conversations is crucial for automated service agents. However, existing intent clustering methods often fail to align with human perceptions due to a heavy reliance on embeddi…
ClusteringCoherence EvaluationIntent DiscoveryText ClusteringHierarchical mixtures of Unigram models for short text clustering: The role of Beta-Liouville priors
This paper presents a variant of the Multinomial mixture model tailored to the unsupervised classification of short text data. While the Multinomial probability vector is traditionally assigned a Dirichlet prior distribu…
Short Text ClusteringText ClusteringVariational Inference