Zero-Shot Relation Extraction from Word Embeddings
Word embeddings learned from text are well-known to capture relational information. However, extracting such relations and their associated vectors is typically performed manually, to illustrate what knowledge is embedded in the space. We propose an automated approach to mine word embeddings for sets of entities of the same type, as well as relationships that hold between them. Our approach starts from a single seed entity and extracts a relational representation from the surrounding vector space. It does so without any relational supervision. Experiments show that our extraction algorithm outperforms spectral clustering and indeed is able to extract high-quality relations from noisy embeddings.