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Topic classification through topic modeling with additive regularization for collection of scientific papersстатья
Информация о цитировании статьи получена из
Web of Science,
Scopus
Дата последнего поиска статьи во внешних источниках: 27 мая 2021 г.
- Автор: Krasnov F.
- Сборник: 14TH CENTRAL AND EASTERN EUROPEAN SOFTWARE ENGINEERING CONFERENCE RUSSIA, CEE-SECR 2018
- Серия: ACM International Conference Proceeding Series
- Том: 14
- Год издания: 2018
- Место издания: Moscow
- Первая страница: 12
- Последняя страница: 13
- DOI: 10.1145/3290621.3290629
- Аннотация: Any text can be presented as an infinite number of topic models.Any of these topics would not have any attributes that would make it possible to break them up into classes. The Author has suggested additive regularization when creating models to single out topic clusters from the Probabilistic Latent Semantic Analysis, PLSA.The method proposed by the Author allows singling out topic classes based on their density in the document-topic space (Matrix Θ) for a selected collection of documents.Such segmentation is similar to hierarchical variant LDA (HLDA) but has no such drawback as that the LDA models have.
- Добавил в систему: Краснов Федор Владимирович