Year: 2019
Type: article
Abstract: We apply hyperbolic embeddings to trace the dynamics of change of conceptualsemantic relationships in a large diachronic scientific corpus (200 years).Our focus is on emerging scientific fields and the increasingly specialized terminology establishing around them.Reproducing high-quality hierarchical structures such as WordNet on a diachronic scale is a very difficult task.Hyperbolic embeddings can map partial graphs into low dimensional, continuous hierarchical spaces, making more explicit the latent structure of the input.We show that starting from simple lists of word pairs (rather than a list of entities with directional links) it is possible to build diachronic hierarchical semantic spaces which allow us to model a process towards specialization for selected scientific fields.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2982658958