SOTAVerified

Topic Models

A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for the discovery of hidden semantic structures in a text body.

Papers

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Showing 1–10 of 881 papers

TitleStatusHype
Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language ModelsCode0
Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority TopicsCode0
topicwizard -- a Modern, Model-agnostic Framework for Topic Model Visualization and Interpretation—0
HAMLET: Healthcare-focused Adaptive Multilingual Learning Embedding-based Topic Modeling—0
Fully Bayesian Approaches to Topics over Time—0
Evaluating Negative Sampling Approaches for Neural Topic ModelsCode0
Multivariate Gaussian Topic Modelling: A novel approach to discover topics with greater semantic coherence—0
Seeded Poisson Factorization: Leveraging domain knowledge to fit topic modelsCode0
Unsupervised Topic Models are Data Mixers for Pre-training Language Models—0
Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of LLMs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NVDMTest perplexity836—Unverified
2Bayesian SMMTest perplexity515—Unverified