@inproceedings{marinho-etal-2019-hierarchical,
title = "Hierarchical Nested Named Entity Recognition",
author = "Marinho, Zita and
Mendes, Afonso and
Miranda, Sebasti{\~a}o and
Nogueira, David",
editor = "Rumshisky, Anna and
Roberts, Kirk and
Bethard, Steven and
Naumann, Tristan",
booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1904",
doi = "10.18653/v1/W19-1904",
pages = "28--34",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Hierarchical Nested Named Entity Recognition
%A Marinho, Zita
%A Mendes, Afonso
%A Miranda, Sebastião
%A Nogueira, David
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 2nd Clinical Natural Language Processing Workshop
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F marinho-etal-2019-hierarchical
%X 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.
%R 10.18653/v1/W19-1904
%U https://aclanthology.org/W19-1904
%U https://doi.org/10.18653/v1/W19-1904
%P 28-34
Markdown (Informal)
[Hierarchical Nested Named Entity Recognition](https://aclanthology.org/W19-1904) (Marinho et al., ClinicalNLP 2019)
ACL
- Zita Marinho, Afonso Mendes, Sebastião Miranda, and David Nogueira. 2019. Hierarchical Nested Named Entity Recognition. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 28–34, Minneapolis, Minnesota, USA. Association for Computational Linguistics.