Subspace-based Set Operations on a Pre-trained Word Embedding Space

Abstract

Word embedding is a fundamental technology in natural language processing. Representation learning for words has been progressing; however, word set operations in embedding spaces have not been defined yet, although they are often performed on sets of words. In this study, we aim to compute representations of sets and set operations using only pre-trained word embeddings. We focused on quantum logic and demonstrated that quantum logic can function as a linguistic set operation in the embedding space.