Recognizing Complex Entity Mentions: A Review and Future Directions

Xiang Dai


Abstract
Standard named entity recognizers can effectively recognize entity mentions that consist of contiguous tokens and do not overlap with each other. However, in practice, there are many domains, such as the biomedical domain, in which there are nested, overlapping, and discontinuous entity mentions. These complex mentions cannot be directly recognized by conventional sequence tagging models because they may break the assumptions based on which sequence tagging techniques are built. We review the existing methods which are revised to tackle complex entity mentions and categorize them as tokenlevel and sentence-level approaches. We then identify the research gap, and discuss some directions that we are exploring.
Anthology ID:
P18-3006
Volume:
Proceedings of ACL 2018, Student Research Workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
37–44
Language:
URL:
https://aclanthology.org/P18-3006
DOI:
10.18653/v1/P18-3006
Bibkey:
Cite (ACL):
Xiang Dai. 2018. Recognizing Complex Entity Mentions: A Review and Future Directions. In Proceedings of ACL 2018, Student Research Workshop, pages 37–44, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Recognizing Complex Entity Mentions: A Review and Future Directions (Dai, ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P18-3006.pdf