Word associations, i.e., spontaneous responses to a cue word, provide not only a window into the human mental lexicon but have also been shown to be a repository of common-sense knowledge and can underpin efforts in lexicography and the construction of dictionaries. Especially the latter tasks require knowledge about the relations underlying the associations (e.g., Taxonomic vs. Situational); however, to date, there is neither an established ontology of relations nor an effective labelling paradigm. Here, we test GPT-4’s ability to infer semantic relations for human-produced word associations. We use four human-labelled data sets of word associations and semantic features, with differing relation inventories and various levels of annotator agreement. We directly prompt GPT-4 with detailed relation definitions without further fine-tuning or training. Our results show that while GPT-4 provided a good account of higher-level classifications (e.g. Taxonomic vs Situational), prompting instructions alone cannot obtain similar performance for detailed classifications (e.g. superordinate, subordinate or coordinate relations) despite high agreement among human annotators. This suggests that latent relations can at least be partially recovered from word associations and highlights ways in which LLMs could be improved and human annotation protocols could adapted to reduce coding ambiguity.

Word embeddings are widely used for diverse applications in natural language processing. Despite extensive research, it is unclear when they succeed or fail to capture human judgements of semantic relatedness and similarity. In this study, we examine a range of models and experimental datasets, showing that while current embeddings perform reasonably well overall, they are unable to account for human judgements of antonyms and polysemy. We suggest that word embeddings perform poorly in representing polysemy and antonymy because they do not consider the context in which humans make word similarity judgements. In support of this, we further show that incorporating additional context into transformer embeddings using general corpora and lexical dictionaries significantly improves the fit with human judgments. Our results provide insight into two key inadequacies of word embeddings, and highlight the importance of incorporating word context into representations of word meaning when accounting for context-free human similarity judgments.

Word associations are among the most common paradigms to study the human mental lexicon. While their structure and types of associations have been well studied, surprisingly little attention has been given to the question of why participants produce the observed associations. Answering this question would not only advance understanding of human cognition, but could also aid machines in learning and representing basic commonsense knowledge. This paper introduces a large, crowd-sourced data set of English word associations with explanations, labeled with high-level relation types. We present an analysis of the provided explanations, and design several tasks to probe to what extent current pre-trained language models capture the underlying relations. Our experiments show that models struggle to capture the diversity of human associations, suggesting WAX is a rich benchmark for commonsense modeling and generation.

Most distributional lexico-semantic models derive their representations based on external language resources such as text corpora. In this study, we propose that internal language models, that are more closely aligned to the mental representations of words could provide important insights into cognitive science, including linguistics. Doing so allows us to reflect upon theoretical questions regarding the structure of the mental lexicon, and also puts into perspective a number of assumptions underlying recently proposed distributional text-based models. In particular, we focus on word-embedding models which have been proposed to learn aspects of word meaning in a manner similar to humans. These are contrasted with internal language models derived from a new extensive data set of word associations. Using relatedness and similarity judgments we evaluate these models and find that the word-association-based internal language models consistently outperform current state-of-the art text-based external language models, often with a large margin. These results are not just a performance improvement; they also have implications for our understanding of how distributional knowledge is used by people.

Semantic similarity is a key issue in many computational tasks. This paper goes into the development and evaluation of two common ways of automatically calculating the semantic similarity between two words. On the one hand, such methods may depend on a manually constructed thesaurus like (Euro)WordNet. Their performance is often evaluated on the basis of a very restricted set of human similarity ratings. On the other hand, corpus-based methods rely on the distribution of two words in a corpus to determine their similarity. Their performance is generally quantified through a comparison with the judgements of the first type of approach. This paper introduces a new Gold Standard of more than 5,000 human intra-category similarity judgements. We show that corpus-based methods often outperform (Euro)WordNet on this data set, and that the use of the latter as a Gold Standard for the former, is thus often far from ideal.