Fast and modular regularized topic modelling
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 probabil...
Main Authors: | Denis Kochedykov, Murat Apishev, Lev Golitsyn, Konstantin Vorontsov |
---|---|
Format: | Article |
Language: | English |
Published: |
FRUCT
2017-11-01
|
Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
Subjects: | |
Online Access: | https://fruct.org/publications/fruct21/files/Koc.pdf
|
Similar Items
-
Regularized Multimodal Hierarchical Topic Model for Document-by-Document Exploratory Search
by: Anastasia Ianina, et al.
Published: (2019-11-01) -
Learning Topic Models With Arbitrary Loss
by: Murat Apishev, et al.
Published: (2020-04-01) -
Advanced Range Telemetry (ARTM) Systems Integration at Edwards AFB
by: Briggs, James R.
Published: (2000) -
Regularization, robustness and sparsity of probabilistic topic models
by: Konstantin Vyacheslavovich Vorontsov, et al.
Published: (2012-12-01) -
Advanced Range Telemetry (ARTM) Systems Integration at the Air Force Flight Test Center
by: Briggs, James R.
Published: (2001)