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 601–625 of 881 papers

TitleStatusHype
Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling—0
Two to Five Truths in Non-Negative Matrix Factorization—0
An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models—0
Understanding MOOC Discussion Forums using Seeded LDA—0
Unification of HDP and LDA Models for Optimal Topic Clustering of Subject Specific Question Banks—0
Uniqueness of Tensor Decompositions with Applications to Polynomial Identifiability—0
Unsupervised Alignment of Privacy Policies using Hidden Markov Models—0
Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents—0
Unsupervised Dialogue Act Induction using Gaussian Mixtures—0
Unsupervised Document Classification with Informed Topic Models—0
Unsupervised Estimation of Word Usage Similarity—0
Unsupervised learning of rhetorical structure with un-topic models—0
Unsupervised Relation Extraction with General Domain Knowledge—0
Unsupervised Topic Modeling Approaches to Decision Summarization in Spoken Meetings—0
Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words—0
Unsupervised Topic Models are Data Mixers for Pre-training Language Models—0
Unveiling the semantic structure of text documents using paragraph-aware Topic Models—0
Updating Rare Term Vector Replacement—0
Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation—0
User Based Aggregation for Biterm Topic Model—0
User Clustering in Online Advertising via Topic Models—0
User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics—0
Using Multilingual Topic Models for Improved Alignment in English-Hindi MT—0
Using neural topic models to track context shifts of words: a case study of COVID-related terms before and after the lockdown in April 2020—0
Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics—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