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 351–375 of 881 papers

TitleStatusHype
WEAKLY SEMI-SUPERVISED NEURAL TOPIC MODELS—0
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsCode0
TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts—0
Vector space explorations of literary languageCode0
Towards Autoencoding Variational Inference for Aspect-based Opinion SummaryCode0
A new evaluation framework for topic modeling algorithms based on synthetic corporaCode0
Dirichlet Variational AutoencoderCode0
Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings—0
Structured Neural Topic Models for Reviews—0
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments—0
A Joint Model of Conversational Discourse Latent Topics on Microblogs—0
Multilingual Anchoring: Interactive Topic Modeling and Alignment Across LanguagesCode0
Construction and Quality Evaluation of Heterogeneous Hierarchical Topic ModelsCode0
Dirichlet belief networks for topic structure learningCode0
SyntaViz: Visualizing Voice Queries through a Syntax-Driven Hierarchical OntologyCode0
Visualization of the Topic Space of Argument Search Results in args.me—0
ATM:Adversarial-neural Topic Model—0
A latent topic model for mining heterogenous non-randomly missing electronic health records data—0
Topic representation: finding more representative words in topic models—0
Contextual Topic Modeling For Dialog Systems—0
Improving Topic Models with Latent Feature Word Representations—0
An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models—0
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of DocumentsCode0
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional PriorCode0
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking—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