Abstract
This paper shines a light on the potential of definition-based semantic models for detecting idiomatic and semi-idiomatic multiword expressions (MWEs) in clinical terminology. Our study focuses on biomedical entities defined in the UMLS ontology and aims to help prioritize the translation efforts of these entities. In particular, we develop an effective tool for scoring the idiomaticity of biomedical MWEs based on the degree of similarity between the semantic representations of those MWEs and a weighted average of the representation of their constituents. We achieve this using a biomedical language model trained to produce similar representations for entity names and their definitions, called BioLORD. The importance of this definition-based approach is highlighted by comparing the BioLORD model to two other state-of-the-art biomedical language models based on Transformer: SapBERT and CODER. Our results show that the BioLORD model has a strong ability to identify idiomatic MWEs, not replicated in other models. Our corpus-free idiomaticity estimation helps ontology translators to focus on more challenging MWEs.- Anthology ID:
- 2023.mwe-1.11
- Volume:
- Proceedings of the 19th Workshop on Multiword Expressions (MWE 2023)
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Archna Bhatia, Kilian Evang, Marcos Garcia, Voula Giouli, Lifeng Han, Shiva Taslimipoor
- Venue:
- MWE
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 73–80
- Language:
- URL:
- https://aclanthology.org/2023.mwe-1.11
- DOI:
- 10.18653/v1/2023.mwe-1.11
- Cite (ACL):
- François Remy, Alfiya Khabibullina, and Thomas Demeester. 2023. Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning. In Proceedings of the 19th Workshop on Multiword Expressions (MWE 2023), pages 73–80, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning (Remy et al., MWE 2023)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2023.mwe-1.11.pdf