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 501–525 of 881 papers

TitleStatusHype
Improving Topic Coherence with Regularized Topic Models—0
Improving Topic Model Clustering of Newspaper Comments for Summarisation—0
Improving Topic Models with Latent Feature Word Representations—0
Improving Update Summarization via Supervised ILP and Sentence Reranking—0
Improving Users' Demographic Prediction via the Videos They Talk about—0
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses—0
Incorporating Lexical Priors into Topic Models—0
Incorporating Word Correlation Knowledge into Topic Modeling—0
Indirect Identification of Psychosocial Risks from Natural Language—0
Information Bottleneck Inspired Method For Chat Text Segmentation—0
Integrating Document Clustering and Topic Modeling—0
Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs—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
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections—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
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics—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