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Modelling taxonomic and thematic relatedness is important for building AIwith comprehensive natural language understanding. The goal of this paper is tolearn more about how taxonomic information is structurally encoded inembeddings. To do this, we design a new hypernym-hyponym probing task andperform a comparative probing study of taxonomic and thematic SGNS and GloVeembeddings. Our experiments indicate that both types of embeddings encode sometaxonomic information, but the amount, as well as the geometric properties ofthe encodings, are independently related to both the encoder architecture, aswell as the embedding training data. Specifically, we find that only taxonomicembeddings carry taxonomic information in their norm, which is determined bythe underlying distribution in the data.

Probing Taxonomic and Thematic Embeddings for Taxonomic Information