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 526–550 of 881 papers

TitleStatusHype
Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world—0
Is Your Anchor Going Up or Down? Fast and Accurate Supervised Topic Models—0
ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository—0
Jointly Learning Word Embeddings and Latent Topics—0
KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts—0
Knowledge-based Word Sense Disambiguation using Topic Models—0
Labeled Anchors and a Scalable, Transparent, and Interactive Classifier—0
Labeled Interactive Topic Models—0
Labeling Topics with Images using Neural Networks—0
Labelling Topics using Unsupervised Graph-based Methods—0
La g\'en\'eration automatique de po\'esie en fran (Automatic Poetry Generation in French)—0
Language Models Explain Word Reading Times Better Than Empirical Predictability—0
Large Enforced Sparse Non-Negative Matrix Factorization—0
Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of LLMs—0
Large Margin Learning of Upstream Scene Understanding Models—0
Large-Scale Sparse Principal Component Analysis with Application to Text Data—0
Large-Scale Validation of Hypothesis Generation Systems via Candidate Ranking—0
Latent Community Discovery with Network Regularization for Core Actors Clustering—0
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments—0
Latent Space Embedding for Retrieval in Question-Answer Archives—0
Latent Topic Embedding—0
LDA2Net: Digging under the surface of COVID-19 topics in scientific literature—0
LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation—0
LDAvis: A method for visualizing and interpreting topics—0
Learning a Concept Hierarchy from Multi-labeled Documents—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