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 626–650 of 881 papers

TitleStatusHype
Multi-GPU Distributed Parallel Bayesian Differential Topic Modelling—0
A 'Gibbs-Newton' Technique for Enhanced Inference of Multivariate Polya Parameters and Topic Models—0
Large Enforced Sparse Non-Negative Matrix Factorization—0
A General Method for Robust Bayesian Modeling—0
A Historical Analysis of the Field of OR/MS using Topic Models—0
Cross-lingual Pseudo Relevance Feedback Based on Weak Relevant Topic Alignment—0
Pivot-Based Topic Models for Low-Resource Lexicon Extraction—0
Surrounding Word Sense Model for Japanese All-words Word Sense Disambiguation—0
Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks—0
EMNLP versus ACL: Analyzing NLP research over time—0
PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis—0
Document-Level Machine Translation Evaluation with Gist Consistency and Text Cohesion—0
Efficient Methods for Incorporating Knowledge into Topic Models—0
Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors—0
Do Distributed Semantic Models Dream of Electric Sheep? Visualizing Word Representations through Image Synthesis—0
Topic Identification and Discovery on Text and Speech—0
Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery—0
Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related CollectionsCode0
Topic Stability over Noisy Sources—0
LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation—0
Extended Topic Model for Word Dependency—0
Matrix and Tensor Factorization Methods for Natural Language Processing—0
KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts—0
Efficient Methods for Inferring Large Sparse Topic Hierarchies—0
Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress—0
Show:102550
← PrevPage 26 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