Papers by Konstantin Vorontsov

2 papers
TopicNet: Making Additive Regularisation for Topic Modelling Accessible (2020.lrec-1)

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Challenge: TopicNet is a Python module for topic modeling.
Approach: They introduce a Python module for topic modeling that brings regularization topic modeling to non-specialists using a general-purpose language.
Outcome: The proposed module aims to bring topic modeling to non-specialists using a general-purpose language.
Topic Balancing with Additive Regularization of Topic Models (2020.acl-srw)

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Challenge: Existing methods for topic modelling on unbalanced data contain topics in various proportions and documents of the relatively small theme become distributed all over the larger topics instead of being grouped into one topic.
Approach: They propose a new regularizer for topic models on unbalanced data collections . they make sure this regularizer increases the quality of topic models, trained on unstructured data .
Outcome: The proposed method improves the quality of topic models trained on unbalanced datasets.

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