Multilingual Negation Scope Resolution for Clinical Text

Mareike Hartmann, Anders Søgaard


Abstract
Negation scope resolution is key to high-quality information extraction from clinical texts, but so far, efforts to make encoders used for information extraction negation-aware have been limited to English. We present a universal approach to multilingual negation scope resolution, that overcomes the lack of training data by relying on disparate resources in different languages and domains. We evaluate two approaches to learn from these resources, training on combined data and training in a multi-task learning setup. Our experiments show that zero-shot scope resolution in clinical text is possible, and that combining available resources improves performance in most cases.
Anthology ID:
2021.louhi-1.2
Volume:
Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis
Month:
April
Year:
2021
Address:
online
Venues:
EACL | Louhi
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–18
Language:
URL:
https://aclanthology.org/2021.louhi-1.2
DOI:
Bibkey:
Cite (ACL):
Mareike Hartmann and Anders Søgaard. 2021. Multilingual Negation Scope Resolution for Clinical Text. In Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis, pages 7–18, online. Association for Computational Linguistics.
Cite (Informal):
Multilingual Negation Scope Resolution for Clinical Text (Hartmann & Søgaard, Louhi 2021)
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PDF:
https://aclanthology.org/2021.louhi-1.2.pdf