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 276–300 of 881 papers

TitleStatusHype
A Case Study of Analysis of Construals in Language on Social Media Surrounding a Crisis Event—0
Exploring Adaptor Grammars for Native Language Identification—0
Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks—0
Exploring Temporal Patterns in Emergency Department Triage Notes with Topic Models—0
Exploring Thematic Coherence in Fake News—0
Exploring the Technology Landscape through Topic Modeling, Expert Involvement, and Reinforcement Learning—0
CFTM: Continuous time fractional topic model—0
Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis management—0
A Bayesian approach to modeling topic-metadata relationships—0
Exploring Topic-Metadata Relationships with the STM: A Bayesian Approach—0
Extended Topic Model for Word Dependency—0
From the Culinary to the Political Meaning of ``quenelle'' : Using Topic Models For Identifying Novel Senses (De la quenelle culinaire \`a la quenelle politique : identification de changements s\'emantiques \`a l'aide des Topic Models) [in French]—0
Factorized Topic Models—0
Clustering for Simultaneous Extraction of Aspects and Features from Reviews—0
A Domain Adaptation Regularization for Denoising Autoencoders—0
Coarse-grained Cross-lingual Alignment of Comparable Texts with Topic Models and Encyclopedic Knowledge—0
Fast Inference for Interactive Models of Text—0
Fast Learning of Clusters and Topics via Sparse Posteriors—0
Exploiting Fine-grained Syntactic Transfer Features to Predict the Compositionality of German Particle Verbs—0
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models—0
Collective Entity Resolution with Multi-Focal Attention—0
Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information—0
Federated Variational Inference Methods for Structured Latent Variable Models—0
Few-shot Learning for Topic Modeling—0
Exploiting Domain Knowledge in Aspect Extraction—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