A Game Based on Vector Representation of Words

Student: Yuliya Kashinskaya

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

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2024

Word embeddings are a technology in the field of natural language processing (NLP) that represent words as numerical vectors, enabling the analysis of their meaning and relationships. In recent years, this technology has found applications in the gaming industry, helping to create more interactive and engaging games. The aim of this thesis is to develop a game application and determine the most suitable word embedding model for gamification based on semantic similarity. The research includes a comparative analysis of models such as Word2Vec, GloVe, FastText, and transformer-based models, as well as an evaluation of their impact on user experience and game performance.

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