• A
  • A
  • A
  • АБB
  • АБB
  • АБB
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта

Книга

Fast and modular regularized topic modelling 21st Conference of Open Innovations Association, FRUCT 2017; Helsinki; Finland; 6 November 2017 до 10 November 2017; Номер категорииCFP1767Z-ART; Код 134240

Vol. 1. IEEE Computer Society, 2017.
Vorontsov K. V., Kochedykov D., Apishev M., Golitsyn L.

Topic modelling is an area of text mining that has been actively developed in the last 15 years. A probabilistic topic model extracts a set of hidden topics from a collection of text documents. It defines each topic by a probability distribution over words and describes each document with a probability distribution over topics. In applications, there are often many requirements, such as, for example, problem-specific knowledge and additional data, to be taken into account. Therefore, it is natural for topic modelling to be considered a multiobjective optimization problem. However, historically, Bayesian learning became the most popular approach for topic modelling. In the Bayesian paradigm, all requirements are formalized in terms of a probabilistic generative process. This approach is not always convenient due to some limitations and technical difficulties. In this work, we develop a non-Bayesian multiobjective approach called the Additive Regularization of Topic Models (ARTM). It is based on regularized Maximum Likelihood Estimation (MLE), and we show that many of the well-known Bayesian topic models can be re-formulated in a much simpler way using the regularization point of view. We review some of the most important types of topic models: multimodal, multilingual, temporal, hierarchical, graph-based, and short-text. The ARTM framework enables easy combination of different types of models to create new models with the desired properties for applications. This modular 'lego-style' technology for topic modelling is implemented in the open-source library BigARTM. © 2017 FRUCT.

Fast and modular regularized topic modelling 21st Conference of Open Innovations Association, FRUCT 2017; Helsinki; Finland; 6 November 2017 до 10 November 2017; Номер категорииCFP1767Z-ART; Код 134240