J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features.

Methods for Named Entity Recognition and Disambiguation (NERD) perform NERand NED in two separate stages. Therefore, NED may be penalized withrespect to precision by NER false positives, and suffers in recall fromNER false negatives. Conversely, NED does not fully exploit informationcomputed by NER such as types of mentions. This paper presents J-NERD, anew approach to perform NER and NED jointly, by means of a probabilisticgraphical model that captures mention spans, mention types, and themapping of mentions to entities in a knowledge base. We presentexperiments with different kinds of texts from the CoNLL’03, ACE’05, andClueWeb’09-FACC1 corpora. J-NERD consistently outperforms state-of-the-artcompetitors in end-to-end NERD precision, recall, and F1.