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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Tsyren Balzhanov
Hashtag Segmentation into Constituent Words
Data Science
(Master’s programme)
2018
Hashtags have an unique communicational function in social media and messenger posts. However, in an computer analysis of social networks they are usually considered as a whole, despite often being a phrase or a short sentence.

In this paper, a problem of segmentation of Russian language hashtag is considered.

This paper contains a comparison of models based on two approaches: an unsupervised one based on using statistical language models, and a supervised approaches based on reccurrent neural networks. Statistical models employ an OpenCorpora corpus of Russian language texts.

In order to compare models and to train neural networks, a large collection of hashtags was collected and marked up with crowdsourcing.

Experimental comparison of models showed that neural network models are better suited for the considered problem than traditional statistical language models. The paper contains an analysis of results with comparison of upsides and downsides of each approach. The paper conculudes with a suggestions for a later model improvements.

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