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 376–400 of 881 papers

TitleStatusHype
Incorporating Lexical Priors into Topic Models—0
Incorporating Word Correlation Knowledge into Topic Modeling—0
Indirect Identification of Psychosocial Risks from Natural Language—0
Deep Probabilistic Graphical Modeling—0
Bootstrapping Translation Detection and Sentence Extraction from Comparable Corpora—0
Information Bottleneck Inspired Method For Chat Text Segmentation—0
Integrating Document Clustering and Topic Modeling—0
Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors—0
Integrating Topic Models and Latent Factors for Recommendation—0
Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process—0
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention—0
Expertise Mining for Enterprise Content Management—0
Interactive Topic Modeling with Anchor Words—0
Interactive Topic Models with Optimal Transport—0
Interactive Visual Exploration of Topic Models using Graphs—0
Inter-Battery Topic Representation Learning—0
Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes—0
Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks—0
Inverted Bilingual Topic Models for Lexicon Extraction from Non-parallel Data—0
Investigating the Impact of Text Summarization on Topic Modeling—0
A Joint Model of Conversational Discourse Latent Topics on Microblogs—0
Knowledge-based Word Sense Disambiguation using Topic Models—0
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics—0
Labeling Topics with Images using Neural Networks—0
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian 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