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 201–225 of 881 papers

TitleStatusHype
Have you tried Neural Topic Models? Comparative Analysis of Neural and Non-Neural Topic Models with Application to COVID-19 Twitter Data—0
Learning a Latent Simplex in Input-Sparsity Time—0
GraphTMT: Unsupervised Graph-based Topic Modeling from Video TranscriptsCode0
Deep Probabilistic Graphical Modeling—0
OCTIS: Comparing and Optimizing Topic models is Simple!Code1
Few-shot Learning for Topic Modeling—0
Hierarchical Topic Presence Models—0
AI supported Topic Modeling using KNIME-Workflows—0
Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic ModelingCode0
A Bayesian approach to modeling topic-metadata relationships—0
Adaptive Mixed Component LDA for Low Resource Topic ModelingCode0
BART-TL: Weakly-Supervised Topic Label Generation—0
Topic Scaling: A Joint Document Scaling -- Topic Model Approach To Learn Time-Specific TopicsCode0
Local and Global Topics in Text Modeling of Web Pages Nested in Web Sites—0
Term-community-based topic detection with variable resolution—0
Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis management—0
Topic Modelling Meets Deep Neural Networks: A Survey—0
Using Transformer based Ensemble Learning to classify Scientific ArticlesCode1
Analysis and tuning of hierarchical topic models based on Renyi entropy approach—0
User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics—0
Learning a Latent Simplex in Input Sparsity Time—0
Exploring Thematic Coherence in Fake News—0
Scalable Cross-lingual Document Similarity through Language-specific Concept HierarchiesCode0
Discovering Airline-Specific Business Intelligence from Online Passenger Reviews: An Unsupervised Text Analytics Approach—0
A Topic Coverage Approach to Evaluation of Topic ModelsCode0
Show:102550
← PrevPage 9 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