Exploration of Application of Multimodal Topic Models on Collection with Metadata

Student: Litvinov Andrey

Supervisor: Nikolay Karpov

Faculty: Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod)

Educational Programme: Computational Linguistics (Master)

Year of Graduation: 2016

Probabilistic topic modellling of textual collections is a modern tool for statistical analysis of texts. In this work we realized the construction of a multimodal topic model based on the EM algorithm. It is compared with basic topic model. The implementation is made using open source framework for topical modelling BigARTM. The experiments were performed on text collections of OHSUMED corpus. Experiments demonstrated that multimodal topic models in BigARTM can achieve quality value similar or better compared with the basic topic models.

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