Subspace-Based Modeling of Conceptual Similarity In Contextual Language Models

We test a computational method that uses projections between subspaces to assess semantic similarity between words in contextual representations. We take embeddings from a text corpus and compare our method to cosine similarity on datasets with similarity evaluations provided by humans, looking at whether it captures asymmetries and order effects.