Word Associations as a Source of Commonsense Knowledge

Abstract

Commonsense knowledge helps individuals naturally make sense of everyday situations and is important for AI systems to truly understand and interact with humans. However, acquiring such knowledge is difficult due to its implicit nature and sheer size, causing existing large-scale commonsense resources to suffer from a sparsity issue. This thesis addresses the challenge of acquiring commonsense knowledge by using word associations, a resource yet untapped for this purpose in natural language processing (NLP). Word associations are spontaneous connections between concepts that individuals make (e.g., smile and happy), reflecting the human mental lexicon. The aim of this thesis is to complement..