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 151–175 of 881 papers

TitleStatusHype
Improving Contextualized Topic Models with Negative SamplingCode0
Improving Neural Topic Models with Wasserstein Knowledge DistillationCode0
Graph neural topic model with commonsense knowledgeCode0
Federated Variational Inference Methods for Structured Latent Variable Models—0
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
ProSiT! Latent Variable Discovery with PROgressive SImilarity ThresholdsCode0
Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature—0
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
Efficient Algorithms for Sparse Moment Problems without Separation—0
Estimation and inference for the Wasserstein distance between mixing measures in topic models—0
Revisiting Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech—0
Towards Better Understanding with Uniformity and Explicit Regularization of Embeddings in Embedding-based Neural Topic Models—0
A Bayesian Topic Model for Human-Evaluated Interpretability—0
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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