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

Dat Ba Nguyen | Zendy; Martin Theobald | Zendy; Gerhard Weikum | Zendy

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

Author(s) -

Dat Ba Nguyen,

Martin Theobald,

Gerhard Weikum

Publication year - 2016

Publication title -

transactions of the association for computational linguistics

Language(s) - English

Resource type - Journals

ISSN - 2307-387X

DOI - 10.1162/tacl_a_00094

Subject(s) - computer science , nerd , entity linking , artificial intelligence , natural language processing , named entity recognition , false positive paradox , exploit , named entity , joint (building) , task (project management) , knowledge base , medicine , computer security , disease , management , gerd , pathology , reflux , economics , architectural engineering , engineering

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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