Modelling the Generation and Retrieval of Word Associations with Word Embeddings

Word associations capture important aspects of the semantic representation of words, by telling us about the contexts in which words appear in the world. Artificially mimicking word associations involves emulating the generation of word associations and the retrieval mechanisms underlying associative responses. Tasks in which this plays a primary role are word-guessing games, such as the Location Taboo Game. In this game, artificial guesser agents should guess the names of cities from simple textual hints and are evaluated with games played by humans. Thus, playing the games successfully requires mimicking associations that humans have with geographical locations. In this thesis, a method for modelling word associations is presented and applied to the construction of an artificial guesser agent for the word-guessing Location Taboo Game.The acquisition of word associations is modelled through the construction of a semantic vector space from a tailored corpus about travel destinations, using contextpredicting distributional semantic models. A targeted corpus annotation method is introduced to make the word associations more explicit. The guesser agent architecture retrieves associations during the game by calculating the associative similarity between a city and a hint from the semantic vector space. The annotation method significantly improves performance. The results on a dataset of example games indicate that the proposed architecture can guess the target city with up to 27.50% accuracy–a substantial improvement over the 5% accuracy achieved by the baseline architecture.