Exploiting anonymous entity mentions for named entity linking

Named entity linking or named entity disambiguation is to link entity mentions to corresponding entities in a knowledge base for resolving the ambiguity of entity mentions. Recently, collective linking methods exploit document-level coherence of the referenced entities by computing a pairwise score between candidates of a pair of named entity mentions (e.g., Raytheon and Boeing) in a document. However, in a document, named entity mentions are significantly less frequent than anonymous entity mentions (e.g., defense contractor and the company). In this paper, we propose a method, DOCument-level Anonymous Entity Type words relatedness (DOC-AET), to exploit the document-level coherence between candidate entities and anonymous entity mentions. We use the anonymous entity type (AET) words to extract anonymous entity mentions. We learn embeddings of AET words from their inter-paragraph co-occurrence matrix; thus, the document-level entity-type relatedness is encoded in the AET word embeddings. Then, we compute the coherence scores between candidate entities and anonymous entity mentions using the AET entity embeddings and document context embeddings. By incorporating such coherence scores for candidates ranking, DOC-AET has achieved new state-of-the-art results on two of the five out-domain test sets for named entity linking.