This thesis deals with associations between words. Describes the design and implementation of a system that can represent a human in the word-association game Codenames. The system uses the Gensim and FastText libraries to create semantic models. The relationship between words is taught by the analysis of the text corpus CWC-2011.

Computational creativity seeks, in part, to develop autonomous agents that exhibit creativity. Language is an ideal creative domain for studying computer agents due to its rich interconnectedness and immense space of possible combinations. This dissertation explores the design, testing, and theory of creative computer systems that write microfiction and play the board game Codenames. The designs of these systems are all similarly based on building up creative artifacts from the underlying structure of the relationships between words. A critical component of the creative process is the ability to evaluate the quality of creative output. Human and automated assessment of our creative systems' outputs yields insights into the challenge of automated creative evaluation. Those insights are formalized into a novel paradigm for designing creative systems and theoretical analyses of the properties of creative domains that facilitate evaluation.

This thesis extends a system of an artificial player of a word-association game Codenames to easy addition of support for new languages. The system is able to play Codenames in roles as a guessing player, a clue giver or, by their combination a Duet version player. For analysis of different languages a neural toolkit Stanza was used, which is language independent and enables automated processing of many languages. It was mainly about lemmatization and part of speech tagging for selection of clues in the game. For evaluation of word associations were several models tested, where the best results had a method Pointwise Mutual Information and predictive model fastText. The system supports playing Codenames in 36 languages comprising 8 different alphabets.