Papers Topic Models
“Topic Models” 태그가 달린 논문 928편 · 필터 해제
BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study
As short text data in native languages like Hindi increasingly appear in modern media, robust methods for topic modeling on such data have gained importance. This study investigates the performance of BERTopic in modelin…
Document EmbeddingTopic ModelsDetecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives
Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Given that language impairments manifest early in NCD progression, visual-stimulated narrative (VSN)-based ana…
Topic ModelsAnalyzing Aviation Safety Narratives with LDA, NMF and PLSA: A Case Study Using Socrata Datasets
This study explores the application of topic modelling techniques Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA) on the Socrata dataset spanni…
Topic ModelsBidirectional Topic Matching: Quantifying Thematic Overlap Between Corpora Through Topic Modelling
This study introduces Bidirectional Topic Matching (BTM), a novel method for cross-corpus topic modeling that quantifies thematic overlap and divergence between corpora. BTM is a flexible framework that can incorporate v…
ArticlesCross-corpusTopic ModelsEnhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning
Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominan…
Contrastive LearningTopic ModelsRefining Dimensions for Improving Clustering-based Cross-lingual Topic Models
Recent works in clustering-based topic models perform well in monolingual topic identification by introducing a pipeline to cluster the contextualized representations. However, the pipeline is suboptimal in identifying t…
ClusteringTopic ModelsGloCOM: A Short Text Neural Topic Model via Global Clustering Context
Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemming from incomplete reconstruction targe…
ClusteringTopic ModelsLocating the Leading Edge of Cultural Change
Measures of textual similarity and divergence are increasingly used to study cultural change. But which measures align, in practice, with social evidence about change? We apply three different representations of text (to…
Topic ModelsProvable unlearning in topic modeling and downstream tasks
Machine unlearning algorithms are increasingly important as legal concerns arise around the provenance of training data, but verifying the success of unlearning is often difficult. Provable guarantees for unlearning are …
Machine UnlearningTopic ModelsNeural Topic Modeling with Large Language Models in the Loop
Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have demonstrated promising capabilities in to…
Topic coverageTopic ModelsFrom Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models
Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This review visits foundational concepts such …
Model CompressionSentenceTopic ModelsWord EmbeddingsMulti-environment Topic Models
Probabilistic topic models are a powerful tool for extracting latent themes from large text datasets. In many text datasets, we also observe per-document covariates (e.g., source, style, political affiliation) that act a…
Topic ModelsReliability of Topic Modeling
Topic models allow researchers to extract latent factors from text data and use those variables in downstream statistical analyses. However, these methodologies can vary significantly due to initialization differences, r…
Topic ModelsEvolving Voices Based on Temporal Poisson Factorisation
The world is evolving and so is the vocabulary used to discuss topics in speech. Analysing political speech data from more than 30 years requires the use of flexible topic models to uncover the latent topics and their ch…
Topic ModelsVariational InferenceTethering Broken Themes: Aligning Neural Topic Models with Labels and Authors
Topic models are a popular approach for extracting semantic information from large document collections. However, recent studies suggest that the topics generated by these models often do not align well with human intent…
Topic ModelsScalable Weibull Graph Attention Autoencoder for Modeling Document Networks
Although existing variational graph autoencoders (VGAEs) have been widely used for modeling and generating graph-structured data, most of them are still not flexible enough to approximate the sparse and skewed latent nod…
DecoderGraph AttentionTopic ModelsMore than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram
To address the increasing prevalence of (audio-)visual data on social media, and to capture the evolving and dynamic nature of this communication, researchers have begun to explore the potential of unsupervised approache…
Topic ModelsEnhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs
Topic modeling is a powerful technique for uncovering hidden themes within a collection of documents. However, the effectiveness of traditional topic models often relies on sufficient word co-occurrence, which is lacking…
Language ModelingLanguage ModellingTopic ModelsEmbedded Topic Models Enhanced by Wikification
Topic modeling analyzes a collection of documents to learn meaningful patterns of words. However, previous topic models consider only the spelling of words and do not take into consideration the homography of words. In t…
ArticlesTime SeriesTopic ModelsAutoTM 2.0: Automatic Topic Modeling Framework for Documents Analysis
In this work, we present an AutoTM 2.0 framework for optimizing additively regularized topic models. Comparing to the previous version, this version includes such valuable improvements as novel optimization pipeline, LLM…
Topic Models