Towards Mining Dialogue Scenarios from Plays: Clustering, Classification and Semantic Labeling of Play Graphs

Student: Vlasov Vladimir

Educational Programme: Computational Linguistics (Master)

Final Grade: 7

Year of Graduation: 2020

This paper describes a method for creating a clustering model for phrases of dialogues and short dialogues consisting of three phrases. As a part of this work, was created an approach to clustering dialog phrases based on vectors from the Cross-language Transformer/CNN encoder for sentences and the KMeans clustering method. Then, using a language model based on a recurrent neural network, a vector presentation of short dialogues was obtained, which were then also clustered. The research was conducted at the Russian drama corpus (RusDraCor), and its results can be used both in the field of digital humanities and in the commercial field for semi-automatic marking of dialogues.

Full text (added June 4, 2020)

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