Joint Named Entity Recognition and Disambiguation

Extracting named entities in text and link-ing extracted names to a given knowledge base are fundamental tasks in applications for text understanding. Existing systems typically run a named entity recognition (NER) model to extract entity names first, then run an entity linking model to link ex-tracted names to a knowledge base. NER and linking models are usually trained sep-arately, and the mutual dependency be-tween the two tasks is ignored. We pro-pose JERL, Joint Entity Recognition and Linking, to jointly model NER and link-ing tasks and capture the mutual depen-dency between them. It allows the in-formation from each task to improve the performance of the other. To the best of our knowledge, JERL is the first model to jointly optimize NER and linking tasks to-gether completely. In experiments on the CoNLL’03/AIDA data set, JERL outper-forms state-of-art NER and linking sys-tems, and we find improvements of 0.4% absolute F1 for NER on CoNLL’03, and 0.36 % absolute precision@1 for linking on AIDA.