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Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings
We show that this leads to embeddings which capture a more diverse range of commonsense properties, and consistently improves results in downstream tasks such as ultra-fine entity typing and ontology completion.
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
We focus in particular on the task of ranking entities according to a given conceptual space dimension.
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies
We show that by augmenting the label set with shared properties, we can improve the performance of the state-of-the-art models for this task.
Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces
The theory of Conceptual Spaces is an influential cognitive-linguistic framework for representing the meaning of concepts.
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