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 101–125 of 881 papers

TitleStatusHype
Improving Contextualized Topic Models with Negative SamplingCode0
Improving Neural Topic Models with Wasserstein Knowledge DistillationCode0
No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial LearningCode1
Graph neural topic model with commonsense knowledgeCode0
Federated Variational Inference Methods for Structured Latent Variable Models—0
ANTM: An Aligned Neural Topic Model for Exploring Evolving TopicsCode1
You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis—0
Neural Dynamic Focused Topic ModelCode0
Improving the Inference of Topic Models via Infinite Latent State Replications—0
Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes—0
Topics in Contextualised Attention Embeddings—0
Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural NetworksCode0
Federated Neural Topic ModelsCode0
Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics—0
The future is different: Large pre-trained language models fail in prediction tasks—0
Moving beyond word lists: towards abstractive topic labels for human-like topics of scientific documents—0
Are Neural Topic Models Broken?Code1
ProSiT! Latent Variable Discovery with PROgressive SImilarity ThresholdsCode0
HyperMiner: Topic Taxonomy Mining with Hyperbolic EmbeddingCode1
Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature—0
Nonparametric Forest-Structured Neural Topic ModelingCode1
Knowledge-Aware Bayesian Deep Topic ModelCode0
On confidence intervals for precision matrices and the eigendecomposition of covariance matrices—0
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization—0
Searching for Structure in Unfalsifiable ClaimsCode0
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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