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 426–450 of 881 papers

TitleStatusHype
Overlapping Clustering Models, and One (class) SVM to Bind Them All—0
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews—0
Painting Analysis Using Wavelets and Probabilistic Topic Models—0
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space—0
Paying down metadata debt: learning the representation of concepts using topic models—0
Predicting Good Configurations for GitHub and Stack Overflow Topic Models—0
Perplexity on Reduced Corpora—0
PhraseCTM: Correlated Topic Modeling on Phrases within Markov Random Fields—0
PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis—0
Pivot-Based Topic Models for Low-Resource Lexicon Extraction—0
Polylingual Tree-Based Topic Models for Translation Domain Adaptation—0
POS Tagging Experts via Topic Modeling—0
Posterior contraction of the population polytope in finite admixture models—0
Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm—0
Practical issues in developing semantic frameworks for the analysis of verbal fluency data: A Norwegian data case study—0
Precision-Recall Balanced Topic Modelling—0
Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing—0
Prediction-Constrained Topic Models for Antidepressant Recommendation—0
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models—0
Prediction Focused Topic Models for Electronic Health Records—0
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge—0
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge—0
Prior-aware Dual Decomposition: Document-specific Topic Inference for Spectral Topic Models—0
Probabilistic Distributional Semantics with Latent Variable Models—0
Probable convexity and its application to Correlated Topic Models—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