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 101–150 of 881 papers

TitleStatusHype
Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts—0
TopicTag: Automatic Annotation of NMF Topic Models Using Chain of Thought and Prompt Tuning with LLMs—0
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text SpatializationsCode0
Text-to-Battery Recipe: A language modeling-based protocol for automatic battery recipe extraction and retrieval—0
Interactive Topic Models with Optimal Transport—0
Historia Magistra Vitae: Dynamic Topic Modeling of Roman Literature using Neural Embeddings—0
Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccinationCode0
LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language ModelsCode0
RICo: Reddit ideological communitiesCode0
Comprehensive Evaluation of Large Language Models for Topic Modeling—0
Exploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter OptimisationCode0
GINopic: Topic Modeling with Graph Isomorphism NetworkCode0
Neural Multimodal Topic Modeling: A Comprehensive EvaluationCode0
Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMsCode0
Automating the Information Extraction from Semi-Structured Interview TranscriptsCode0
Membership Inference Attacks and Privacy in Topic ModelingCode0
The Geometric Structure of Topic Models—0
Topic Modeling as Multi-Objective Contrastive Optimization—0
RankSum An unsupervised extractive text summarization based on rank fusion—0
Network-based Topic Structure VisualizationCode0
CFTM: Continuous time fractional topic model—0
Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses—0
Topic Modelling: Going Beyond Token Outputs—0
Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model—0
Discovering Significant Topics from Legal Decisions with Selective Inference—0
Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notes—0
Contrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms—0
Revisiting Topic-Guided Language ModelsCode0
Labeled Interactive Topic Models—0
Profiling Irony & Stereotype: Exploring Sentiment, Topic, and Lexical Features—0
Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling—0
Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation—0
TopicAdapt- An Inter-Corpora Topics Adaptation Approach—0
Towards the TopMost: A Topic Modeling System ToolkitCode0
Evaluating Dynamic Topic Models—0
Towards Generalising Neural Topical RepresentationsCode0
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text SpatializationCode0
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport—0
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews—0
Diversity-Aware Coherence Loss for Improving Neural Topic ModelsCode0
Contextualized Topic Coherence MetricsCode0
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic ModelingCode0
HyHTM: Hyperbolic Geometry based Hierarchical Topic ModelsCode0
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections—0
Reinforcement Learning for Topic ModelsCode0
Two to Five Truths in Non-Negative Matrix Factorization—0
Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection—0
A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System—0
Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence—0
Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic ModelingCode0
Show:102550
← PrevPage 3 of 18Next →
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