artm

ARTM, sometimes rendered as ARTM or ARTm, refers to a software toolkit used for topic modeling in natural language processing. It implements the Additive Regularization of Topic Models (ARTM) framework, a methodology that combines multiple regularizers to guide the discovery of topics beyond standard probabilistic topic models. ARTm is commonly used to extract latent topics from large text collections and to organize documents by their topic distributions.

Architecture and approach: The ARTM toolkit centers on a core learning engine with modular components. It supports a range of regularizers that influence topic formation, such as sparsity constraints, decorrelation among topics, and supervision from labeled data. The design emphasizes scalability and flexibility, offering support for streaming data, batching, and parallel computation to handle large corpora. Users configure the number of topics, select regularizers, and set training hyperparameters to shape the resulting topic structure.

Workflow and usage: Data are tokenized and indexed into a dictionary, and documents are converted into batches. The model is configured with topics and regularizers, then run through iterative updates until convergence. The resulting topic distributions for documents and the associated word-topic associations are typically evaluated with coherence measures and downstream performance in downstream tasks such as clustering or feature extraction for machine learning pipelines.

History and context: The ARTM framework emerged from academic research on topic modeling and has been released as open-source software. It is used in both research and industry contexts to perform scalable, customizable topic modeling and to support exploratory text analysis.

See also: topic modeling, latent Dirichlet allocation, regularization in machine learning.