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 26–50 of 881 papers

TitleStatusHype
Reliability of Topic ModelingCode0
Evolving Voices Based on Temporal Poisson Factorisation—0
Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authors—0
Scalable Weibull Graph Attention Autoencoder for Modeling Document NetworksCode0
More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram—0
Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs—0
Embedded Topic Models Enhanced by Wikification—0
AutoTM 2.0: Automatic Topic Modeling Framework for Documents Analysis—0
NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization—0
Investigating the Impact of Text Summarization on Topic Modeling—0
Iterative Improvement of an Additively Regularized Topic ModelCode1
Topic Modeling with Fine-tuning LLMs and Bag of SentencesCode0
Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts—0
TopicTag: Automatic Annotation of NMF Topic Models Using Chain of Thought and Prompt Tuning with LLMs—0
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text SpatializationsCode0
Text-to-Battery Recipe: A language modeling-based protocol for automatic battery recipe extraction and retrieval—0
Interactive Topic Models with Optimal Transport—0
Historia Magistra Vitae: Dynamic Topic Modeling of Roman Literature using Neural Embeddings—0
Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccinationCode0
Determination of the Number of Topics Intrinsically: Is It Possible?Code1
S^3 -- Semantic Signal SeparationCode2
LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language ModelsCode0
RICo: Reddit ideological communitiesCode0
Comprehensive Evaluation of Large Language Models for Topic Modeling—0
FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic ModelCode2
Show:102550
← PrevPage 2 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