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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 881 papers

TitleStatusHype
TopicGPT: A Prompt-based Topic Modeling FrameworkCode2
Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling—0
DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLMCode1
Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation—0
TopicAdapt- An Inter-Corpora Topics Adaptation Approach—0
Towards the TopMost: A Topic Modeling System ToolkitCode0
Evaluating Dynamic Topic Models—0
Towards Generalising Neural Topical RepresentationsCode0
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text SpatializationCode0
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport—0
Effective Neural Topic Modeling with Embedding Clustering RegularizationCode1
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews—0
Diversity-Aware Coherence Loss for Improving Neural Topic ModelsCode0
Contextualized Topic Coherence MetricsCode0
Revisiting Automated Topic Model Evaluation with Large Language ModelsCode1
HyHTM: Hyperbolic Geometry based Hierarchical Topic ModelsCode0
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic ModelingCode0
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections—0
Reinforcement Learning for Topic ModelsCode0
Two to Five Truths in Non-Negative Matrix Factorization—0
Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection—0
InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic ModelingCode1
A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System—0
Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence—0
Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic ModelingCode0
Show:102550
← PrevPage 4 of 36Next →
All datasets20NewsGroupsAG News20 NewsgroupsArxiv HEP-TH citation graphAgNewsNYT

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1vONTSSC_v0.69—Unverified
2GSMC_v0.55—Unverified
3vNVDMC_v0.52—Unverified
4ETMC_v0.51—Unverified
5NSTMC_v0.38—Unverified
6ProdLDAC_v0.35—Unverified
#ModelMetricClaimedVerifiedStatus
1vONTSSC_v0.49—Unverified
2vNVDMC_v0.44—Unverified
3GSMC_v0.41—Unverified
4ETMC_v0.41—Unverified
5NSTMC_v0.37—Unverified
6ProdLDAC_v0.32—Unverified
#ModelMetricClaimedVerifiedStatus
1NVDMTest perplexity836—Unverified
2Bayesian SMMTest perplexity515—Unverified
#ModelMetricClaimedVerifiedStatus
1JoSHMACC83.24—Unverified
2TopicEqTopic Coherence@500.1—Unverified
#ModelMetricClaimedVerifiedStatus
1vONTSSC_v0.49—Unverified
#ModelMetricClaimedVerifiedStatus
1JoSHMACC90.91—Unverified