Hierarchical Nested Named Entity Recognition

Zita Marinho, Afonso Mendes, Sebastião Miranda, David Nogueira


Abstract
In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in mentions, such as those related to specific symptoms or diseases associated with different anatomical regions. Unlike previous approaches, we build a transition-based parser that explicitly models an arbitrary number of hierarchical and nested mentions, and propose a loss that encourages correct predictions of higher-level mentions. We further introduce a set of modifier classes which introduces certain concepts that change the meaning of an entity, such as absence, or uncertainty about a given disease. Our proposed model achieves state-of-the-art results in medical entity recognition datasets, using both nested and hierarchical mentions.
Anthology ID:
W19-1904
Volume:
Proceedings of the 2nd Clinical Natural Language Processing Workshop
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venues:
ClinicalNLP | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
28–34
Language:
URL:
https://www.aclweb.org/anthology/W19-1904
DOI:
10.18653/v1/W19-1904
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-1904.pdf
Supplementary:
 W19-1904.Supplementary.pdf