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    <abstract>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.</abstract>
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%0 Journal Article
%T J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features
%A Nguyen, Dat Ba
%A Theobald, Martin
%A Weikum, Gerhard
%J Transactions of the Association for Computational Linguistics
%D 2016
%V 4
%I MIT Press
%C Cambridge, MA
%F nguyen-etal-2016-j
%X 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.
%R 10.1162/tacl_a_00094
%U https://aclanthology.org/Q16-1016
%U https://doi.org/10.1162/tacl_a_00094
%P 215-229

Markdown (Informal)

[J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features](https://aclanthology.org/Q16-1016) (Nguyen et al., TACL 2016)

• J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features (Nguyen et al., TACL 2016)

ACL

• Dat Ba Nguyen, Martin Theobald, and Gerhard Weikum. 2016. J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features. Transactions of the Association for Computational Linguistics, 4:215–229.