Papers Topic Models
“Topic Models” 태그가 달린 논문 928편 · 필터 해제
TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale
Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at massive scale in varied forms such as reviews, transcripts, and survey…
Topic ModelsLabel Semantic Expansion via Label Guided Neural Topic Modeling
Topic models are widely used for content analysis, where users often analyze corpora around predefined labels rather than unordered latent topics. Existing label-aware topic models mainly follow a labels-for-topics persp…
Topic ModelsFlesch-Kincaid Readability Depends Only on the Topic Distribution in Long Texts under Topic Models
Flesch Reading Ease (FRE) and the Flesch-Kincaid Grade Level (FKGL) are widely used readability scores for English computed from the same two document statistics, yet their stability on long documents need not imply inva…
Topic ModelsDynamic Topic Modeling for Cross-Corpus Temporal Analysis
Dynamic Embedded Topic Models (D-ETM) provide an interpretable framework for modeling temporal semantic evolution, but cross-corpus comparison remains difficult because topics are often learned independently and aligned …
Topic ModelsDomain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation
Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpretability often degrades on specialized co…
parameter-efficient fine-tuningTopic ModelsWhen Lexical Change Misleads: Rethinking Dynamic Topic Model Evaluation with Traditional and LLM-Based Metrics
Dynamic topic models capture evolving word distributions, but traditional coherence metrics may fail when vocabulary changes while semantic meaning persists. We evaluate 120 topics from CoNTM and DLDA across NYT, DBLP, a…
Semantic SimilarityTopic ModelsSynthetic Consumer Insight Generation with Large Language Models
Modern data-driven marketing relies on large amounts of consumer data, yet collecting such data can be costly, time-consuming, and difficult to scale. This research examines whether large language models (LLMs) can be us…
Topic ModelsAn Information-Geometric Justification for Composite Coherence in Event-Based Narrative Extraction
Graph-based narrative extraction relies on a coherence function to score transitions between events, but the coherence metrics in current use are defined operationally and lack an information-theoretic foundation. We stu…
Topic ModelsBaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning
While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational flexibility and often results in overconf…
parameter-efficient fine-tuningTopic ModelsHybrid Classical-Quantum Variational Autoencoder for Neural Topic Modeling
Neural topic models enable scalable semantic discovery, but their integration with quantum hardware remains largely unexplored. We present a proof-of-concept hybrid classical-quantum variational autoencoder (VAE) for top…
Topic ModelsMoment-Based Inference for Regression with Latent Dirichlet Covariates
Topic models are often used as dimension-reduction tools before regression, with estimated document-level topic shares treated as observed covariates. This plug-in workflow creates two inferential difficulties: valid inf…
Topic ModelsDynamic Topic Modeling with a Higher-Order Hypergraphical Representation
Dynamic topic modeling is widely used to analyze evolving trends in scientific literature, medical records, and social media. Traditional topic models represent each topic through a single probability vector on the multi…
Topic ModelsSlogans or Stance? A Label-Light Diagnostic for Entrepreneurial-Discourse Measurement on Chinese SOE Speeches
Dictionary methods, topic models, and embedding-similarity scorers are widely used in CSS and management research to measure constructs such as "entrepreneurial spirit" in corporate speeches. We contribute a label-light …
Topic ModelsA Comparative Evaluation of Structural Topic Models and BERTopic for Short, Open-Ended Survey Responses
Topic modeling in applied psychology increasingly spans two methodological traditions: probabilistic bag-of-words models and newer embedding-based approaches. Yet many evaluations of these methods rely on longer and clea…
Topic ModelsLLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations
This paper examines the opportunities, limitations, and practical considerations associated with the use of large language models (LLMs) in qualitative research. Drawing on a multidisciplinary perspective that combines e…
Topic ModelsLLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models
Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and often yield incoherent or weakly aligned topics. Recent LLM-based re…
Topic ModelsRetrieving Floods without Floodlights: Topic Models as Binary Classifiers for Extreme Climate Events in German News
In studies of media coverage of extreme climate events, NLP methods have become indispensable for identifying relevant texts in large news databases. Still, enough annotated data to train accurate deep learning-based cla…
Topic ModelsProposing Topic Models and Evaluation Frameworks for Analyzing Associations with External Outcomes: An Application to Leadership Analysis Using Large-Scale Corporate Review Data
Analyzing topics extracted from text data in relation to external outcomes is important across fields such as computational social science and organizational research. However, existing topic modeling methods struggle to…
Topic ModelsReasoning-Based Refinement of Unsupervised Text Clusters with LLMs
Unsupervised methods are widely used to induce latent semantic structure from large text collections, yet their outputs often contain incoherent, redundant, or poorly grounded clusters that are difficult to validate with…
Representation LearningTopic ModelsPRISM: LLM-Guided Semantic Clustering for High-Precision Topics
In this paper, we propose Precision-Informed Semantic Modeling (PRISM), a structured topic modeling framework combining the benefits of rich representations captured by LLMs with the low cost and interpretability of late…
Topic Models