Topic Models
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
Software Framework for Topic Modelling with Large Corpora
Large corpora are ubiquitous in today’s world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM).
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
Distributed dense word vectors have been shown to be effective at capturing token-level semantic and syntactic regularities in language, while topic models can form interpretable representations over documents.
Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering
In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models.
Autoencoding Variational Inference For Topic Models
• akashgit/autoencoding_vi_for_topic_models
• 
A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven diffi- cult to apply to topic models in practice.
Computing Web-scale Topic Models using an Asynchronous Parameter Server
Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery.
Topic Modeling in Embedding Spaces
• adjidieng/ETM
• 
To this end, we develop the Embedded Topic Model (ETM), a generative model of documents that marries traditional topic models with word embeddings.
KATE: K-Competitive Autoencoder for Text
• hugochan/KATE
•
Autoencoders have been successful in learning meaningful representations from image datasets.