Topic Models
210 papers with code • 6 benchmarks • 12 datasets
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.
Benchmarks
These leaderboards are used to track progress in Topic Models
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Libraries
Use these libraries to find Topic Models models and implementationsDatasets
AG News
Reddit
New York Times Annotated Corpus
Arxiv HEP-TH citation graph
OpoSum
Reddit Ideology Database
Most implemented papers
Latent Dirichlet Allocation
Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.
Learning Topic Models - Going beyond SVD
Topic Modeling is an approach used for automatic comprehension and classification of data in a variety of settings, and perhaps the canonical application is in uncovering thematic structure in a corpus of documents.
Stochastic Variational Inference
We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions.
A Practical Algorithm for Topic Modeling with Provable Guarantees
Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora.
Partial Membership Latent Dirichlet Allocation
Topic models (e. g., pLSA, LDA, SLDA) have been widely used for segmenting imagery.
Partial Membership Latent Dirichlet Allocation
Topic models (e. g., pLSA, LDA, sLDA) have been widely used for segmenting imagery.
Topic Modeling based on Keywords and Context
Current topic models often suffer from discovering topics not matching human intuition, unnatural switching of topics within documents and high computational demands.
Coherence-Aware Neural Topic Modeling
YongfeiYan/Neural-Document-Modeling
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EMNLP 2018
Topic models are evaluated based on their ability to describe documents well (i. e. low perplexity) and to produce topics that carry coherent semantic meaning.
Dirichlet belief networks for topic structure learning
Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures.
Learning document embeddings along with their uncertainties
skesiraju/BaySMM
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We present Bayesian subspace multinomial model (Bayesian SMM), a generative log-linear model that learns to represent documents in the form of Gaussian distributions, thereby encoding the uncertainty in its co-variance.
