Hierarchical Nested Named Entity Recognition
Zita Marinho, Afonso Mendes, Sebastião Miranda, David Silva Nogueira · 2019
In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in entity 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 propose 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 model achieves state-of-the-art results in medical entity recognition datasets, using both nested and hierarchical mentions.