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
Latest papers
TopicGPT: A Prompt-based Topic Modeling Framework
Topic modeling is a well-established technique for exploring text corpora.
DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM
amazon-science/text_generation_diffusion_llm_topic
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Additionally, by exploiting the power of diffusion model, our framework also provides the capability to do topic based text generation.
Towards the TopMost: A Topic Modeling System Toolkit
bobxwu/topmost
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Topic models have been proposed for decades with various applications and recently refreshed by the neural variational inference.
Towards Generalising Neural Topical Representations
xiaohao-yang/topic_model_generalisation
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To do so, we propose to enhance NTMs by narrowing the semantical distance between similar documents, with the underlying assumption that documents from different corpora may share similar semantics.
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization
Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting semantic similarity between the documents and supporting corpus analysis.
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporating human knowledge.
Effective Neural Topic Modeling with Embedding Clustering Regularization
bobxwu/ecrtm
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Topic models have been prevalent for decades with various applications.
Diversity-Aware Coherence Loss for Improving Neural Topic Models
raymondzmc/topic-model-diversity-aware-coherence-loss
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The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss.
Contextualized Topic Coherence Metrics
hamedr96/ctc
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The recent explosion in work on neural topic modeling has been criticized for optimizing automated topic evaluation metrics at the expense of actual meaningful topic identification.
Revisiting Automated Topic Model Evaluation with Large Language Models
Topic models are used to make sense of large text collections.
