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

Dat Ba Nguyen, Martin Theobald, Gerhard Weikum


Methods for Named Entity Recognition and Disambiguation (NERD) perform NER
and NED in two separate stages. Therefore, NED may be penalized with
respect to precision by NER false positives, and suffers in recall from
NER false negatives. Conversely, NED does not fully exploit information
computed by NER such as types of mentions. This paper presents J-NERD, a
new approach to perform NER and NED jointly, by means of a probabilistic
graphical model that captures mention spans, mention types, and the
mapping of mentions to entities in a knowledge base. We present
experiments with different kinds of texts from the CoNLL’03, ACE’05, and
ClueWeb’09-FACC1 corpora. J-NERD consistently outperforms state-of-the-art
competitors in end-to-end NERD precision, recall, and F1.


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