Papers Topic Models
“Topic Models” 태그가 달린 논문 928편 · 필터 해제
Holistic Evaluations of Topic Models
Topic models are gaining increasing commercial and academic interest for their ability to summarize large volumes of unstructured text. As unsupervised machine learning methods, they enable researchers to explore data an…
Topic ModelsCombining Language and Topic Models for Hierarchical Text Classification
Hierarchical text classification (HTC) is a natural language processing task which has the objective of categorising text documents into a set of classes from a predefined structured class hierarchy. Recent HTC approache…
Multi-Label Text ClassificationNatural Language UnderstandingTopic ModelsS2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling
Modeling latent representations in a hyperspherical space has proven effective for capturing directional similarities in high-dimensional text data, benefiting topic modeling. Variational autoencoder-based neural topic m…
Topic ModelsscE2TM improves single-cell embedding interpretability and reveals cellular perturbation signatures
Single-cell RNA sequencing technologies have revolutionized our understanding of cellular heterogeneity, yet computational methods often struggle to balance performance with biological interpretability. Embedded topic mo…
Topic ModelsSemantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
Latent Dirichlet Allocation (LDA) is a prominent generative probabilistic model used for uncovering abstract topics within document collections. In this paper, we explore the effectiveness of augmenting topic models with…
Coherence EvaluationTopic ModelsDTECT: Dynamic Topic Explorer & Context Tracker
The explosive growth of textual data over time presents a significant challenge in uncovering evolving themes and trends. Existing dynamic topic modeling techniques, while powerful, often exist in fragmented pipelines th…
Topic ModelsProxAnn: Use-Oriented Evaluations of Topic Models and Document Clustering
Topic model and document-clustering evaluations either use automated metrics that align poorly with human preferences or require expert labels that are intractable to scale. We design a scalable human evaluation protocol…
Topic ModelsNarrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
With rapidly evolving media narratives, it has become increasingly critical to not just extract narratives from a given corpus but rather investigate, how they develop over time. While popular narrative extraction method…
ArticlesChange Point DetectionLanguage ModelingLanguage Modelling+2Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics
Topic models often fail to capture low-prevalence, domain-critical themes, so-called minority topics, such as mental health themes in online comments. While some existing methods can incorporate domain knowledge, such as…
Topic Modelstopicwizard -- a Modern, Model-agnostic Framework for Topic Model Visualization and Interpretation
Topic models are statistical tools that allow their users to gain qualitative and quantitative insights into the contents of textual corpora without the need for close reading. They can be applied in a wide range of sett…
modelTopic ModelsHAMLET: Healthcare-focused Adaptive Multilingual Learning Embedding-based Topic Modeling
Traditional topic models often struggle with contextual nuances and fail to adequately handle polysemy and rare words. This limitation typically results in topics that lack coherence and quality. Large Language Models (L…
Topic ModelsFully Bayesian Approaches to Topics over Time
The Topics over Time (ToT) model captures thematic changes in timestamped datasets by explicitly modeling publication dates jointly with word co-occurrence patterns. However, ToT was not approached in a fully Bayesian fa…
Topic ModelsEvaluating Negative Sampling Approaches for Neural Topic Models
Negative sampling has emerged as an effective technique that enables deep learning models to learn better representations by introducing the paradigm of learn-to-compare. The goal of this approach is to add robustness to…
Document ClassificationTopic ModelsMultivariate Gaussian Topic Modelling: A novel approach to discover topics with greater semantic coherence
An important aspect of text mining involves information retrieval in form of discovery of semantic themes (topics) from documents using topic modelling. While generative topic models like Latent Dirichlet Allocation (LDA…
Information RetrievalTopic ModelsSeeded Poisson Factorization: Leveraging domain knowledge to fit topic models
Topic models are widely used for discovering latent thematic structures in large text corpora, yet traditional unsupervised methods often struggle to align with predefined conceptual domains. This paper introduces Seeded…
Computational EfficiencyTopic ModelsVariational InferenceUnsupervised Topic Models are Data Mixers for Pre-training Language Models
The performance of large language models (LLMs) is significantly affected by the quality and composition of their pre-training data, which is inherently diverse, spanning various domains, sources, and topics. Effectively…
Topic ModelsLarge Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of LLMs
A common use of NLP is to facilitate the understanding of large document collections, with a shift from using traditional topic models to Large Language Models. Yet the effectiveness of using LLM for large corpus underst…
HallucinationTopic ModelsHierarchical Graph Topic Modeling with Topic Tree-based Transformer
Textual documents are commonly connected in a hierarchical graph structure where a central document links to others with an exponentially growing connectivity. Though Hyperbolic Graph Neural Networks (HGNNs) excel at cap…
SpecificityTopic ModelsExploring the Technology Landscape through Topic Modeling, Expert Involvement, and Reinforcement Learning
In today's rapidly evolving technological landscape, organizations face the challenge of integrating external insights into their decision-making processes to stay competitive. To address this issue, this study proposes …
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Topic ModelsVisual Exploration of Stopword Probabilities in Topic Models
Stopword removal is a critical stage in many Machine Learning methods but often receives little consideration, it interferes with the model visualizations and disrupts user confidence. Inappropriately chosen or hastily o…
Topic Models