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Proceedings of the 28th International Conference on Computational Linguistics
Work with neural word embeddings and lexical relations has largely focused on confirmatory experiments which use human-curated examples of semantic and syntactic relations to validate against. In this paper, we explore the degree to which lexical relations, such as those found in popular validation sets, can be derived and extended from a variety of neural embeddings using classical clustering methods. We show that the Word2Vec space of word-pairs (i.e., offset vectors) significantlydoi:10.18653/v1/2020.coling-main.112 fatcat:iaqyb7ag3fhmhcxwr6ivp66pwq