Graph neural entity disambiguation

Entity Disambiguation (ED) aims to automatically resolve mentions of entities in a document to corresponding entries in a given knowledge base. State-of-the-art ED methods typically utilize local contextual information for obtaining mention embeddings which will be compared to candidate entity embeddings and then apply Conditional Random Field (CRF) for collective ED, considering global coherence. An inherent drawback of these methods is that, the global semantic relationships among the candidate entities in the same document are not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the global coherence effect.