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 176–200 of 881 papers

TitleStatusHype
Topic Model Supervised by Understanding Map—0
Learning Topic Models: Identifiability and Finite-Sample Analysis—0
Beyond Topics: Discovering Latent Healthcare Objectives from Event Sequences—0
One Objective for All Models --- Self-supervised Learning for Topic Models—0
Generating Scenes with Latent Object Models—0
Evaluation of Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for Emotion Analysis in Turkish Tweets—0
Towards Explainable Scientific Venue Recommendations—0
Evolution of topics in central bank speech communication—0
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus ExplorationCode1
An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures—0
Apples to Apples: A Systematic Evaluation of Topic ModelsCode1
Chronic Pain and Language: A Topic Modelling Approach to Personal Pain Descriptions—0
Benchmarking Neural Topic Models: An Empirical Study—0
A Case Study of Analysis of Construals in Language on Social Media Surrounding a Crisis Event—0
Spurious Correlations in Cross-Topic Argument MiningCode0
Measuring daily-life fear perception change: a computational study in the context of COVID-19—0
Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations—0
Is Automated Topic Model Evaluation Broken?: The Incoherence of CoherenceCode1
Sawtooth Factorial Topic Embeddings Guided Gamma Belief NetworkCode1
Generalisability of Topic Models in Cross-corpora Abusive Language Detection—0
Topic Model or Topic Twaddle? Re-evaluating Semantic Interpretability Measures—0
A Query-Driven Topic Model—0
Non-negative matrix factorization algorithms greatly improve topic model fitsCode1
Is Automated Topic Model Evaluation Broken? The Incoherence of CoherenceCode1
Have you tried Neural Topic Models? Comparative Analysis of Neural and Non-Neural Topic Models with Application to COVID-19 Twitter Data—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