Computing and Information Systems - Theses
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ItemWord Associations as a Source of Commonsense Knowledge( 2023-12)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 existing resources like commonsense knowledge graphs and pre-trained language models (PLMs), and enhance models’ ability to reason in a more intuitive and human-like manner. To achieve this aim, we explore three aspects of word associations: (1) understanding the relational knowledge they encode, (2) comparing the content and utility for NLP downstream tasks of large-scale word associations with widely-used commonsense knowledge resources, and (3) improving knowledge extraction from PLMs with word associations. We introduce a crowd-sourced large-scale dataset of word association explanations, which is crucial for disambiguating multiple reasons behind word associations. This resource fills a gap in the cognitive psychology community by providing a dataset to study the rationales and structures underlying associations. By automating the process of labelling word associations with relevant relations, we demonstrate that these explanations enhance the performance of relation extractors. We conduct a comprehensive comparison between large-scale word association networks and the ConceptNet commonsense knowledge graph, analysing their structures, knowledge content, and benefits for commonsense reasoning tasks. Even though we identify systematic differences between the two resources, we find that they both show improvements when incorporated into NLP models. Finally, we propose a diagnostic framework to understand the implicit knowledge encoded in PLMs and identify effective strategies for knowledge extraction. We show that word associations can enhance the quality of extracted knowledge from PLMs. The contributions of this thesis highlight the value of word associations in acquiring commonsense knowledge, offering insights into their utility in cognitive psychology and NLP research.