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Hierarchical linear disentanglement of data-driven conceptual spaces

Alshaikh, Rana, Bouraoui, Zied and Schockaert, Steven 2020. Hierarchical linear disentanglement of data-driven conceptual spaces. Presented at: International Joint Conferences on Artificial Intelligence Organization (IJCAI-PRICAI 2020), Yokohama, Japan, 11-17 July 2020.
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Abstract

Conceptual spaces are geometric meaning representations in which similar entities are represented by similar vectors. They are widely used in cognitive science, but there has been relatively little work on learning such representations from data. In particular, while standard representation learning methods can be used to induce vector space embeddings from text corpora, these differ from conceptual spaces in two crucial ways. First, the dimensions of a conceptual space correspond to salient semantic features, known as quality dimensions, whereas the dimensions of learned embeddings typically lack any clear interpretation. This has been partially addressed in previous work, which has shown that it is possible to identify directions in learned vector spaces which capture semantic features. Second, conceptual spaces are normally organised into a set of domains, each of which is associated with a separate vector space. In contrast, learned embeddings represent all entities in a single vector space. Our hypothesis in this paper is that such single-space representations are sub-optimal for learning quality dimensions, due to the fact that semantic features are often only relevant to a subset of the entities. We show that this issue can be mitigated by identifying features in a hierarchical fashion. Intuitively, the top-level features split the vector space into domains,allowing us to subsequently identify domain-specific quality dimensions.

Item Type: Conference or Workshop Item (Paper)
Status: In Press
Schools: Computer Science & Informatics
Additional Information: Conference postponed due to COVID-19 pandemic
Date of First Compliant Deposit: 11 May 2020
Date of Acceptance: 20 April 2020
Last Modified: 11 May 2020 14:04
URI: http://orca.cf.ac.uk/id/eprint/131529

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