An Effective Transition-based Model for Discontinuous NER

Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cecile Paris


Abstract
Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans. Conventional sequence tagging techniques encode Markov assumptions that are efficient but preclude recovery of these mentions. We propose a simple, effective transition-based model with generic neural encoding for discontinuous NER. Through extensive experiments on three biomedical data sets, we show that our model can effectively recognize discontinuous mentions without sacrificing the accuracy on continuous mentions.
Anthology ID:
2020.acl-main.520
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5860–5870
Language:
URL:
https://aclanthology.org/2020.acl-main.520
DOI:
10.18653/v1/2020.acl-main.520
Bibkey:
Cite (ACL):
Xiang Dai, Sarvnaz Karimi, Ben Hachey, and Cecile Paris. 2020. An Effective Transition-based Model for Discontinuous NER. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5860–5870, Online. Association for Computational Linguistics.
Cite (Informal):
An Effective Transition-based Model for Discontinuous NER (Dai et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.520.pdf
Video:
 http://slideslive.com/38928958
Code
 daixiangau/acl2020-transition-discontinuous-ner