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 476–500 of 881 papers

TitleStatusHype
Growing Multi-Domain Glossaries from a Few Seeds using Probabilistic Topic Models—0
HAMLET: Healthcare-focused Adaptive Multilingual Learning Embedding-based Topic Modeling—0
Hashtag Recommendation with Topical Attention-Based LSTM—0
Have you tried Neural Topic Models? Comparative Analysis of Neural and Non-Neural Topic Models with Application to COVID-19 Twitter Data—0
Helping users discover perspectives: Enhancing opinion mining with joint topic models—0
He Said, She Said: Gender in the ACL Anthology—0
Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers—0
Hidden Softmax Sequence Model for Dialogue Structure Analysis—0
Hiearchie: Visualization for Hierarchical Topic Models—0
Hierarchical Discriminative Classification for Text-Based Geolocation—0
Hierarchical Graph Topic Modeling with Topic Tree-based Transformer—0
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity—0
Hierarchical Topic Presence Models—0
Historia Magistra Vitae: Dynamic Topic Modeling of Roman Literature using Neural Embeddings—0
How Text Segmentation Algorithms Gain from Topic Models—0
HTMOT : Hierarchical Topic Modelling Over Time—0
Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters—0
Identifying Comparable Corpora Using LDA—0
Identifying Patterns of Associated-Conditions through Topic Models of Electronic Medical Records—0
Identifying Reference Spans: Topic Modeling and Word Embeddings help IR—0
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation—0
Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration—0
Improving Neural Topic Models by Contrastive Learning with BERT—0
Improving the Inference of Topic Models via Infinite Latent State Replications—0
Improving Topic Coherence with Latent Feature Word Representations in MAP Estimation for Topic Modeling—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