Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning
This work addresses the problem of prioritizing translation efforts for biomedical entities in ontology translation, though it is incremental as it builds on existing language models.
The paper tackled detecting idiomatic multiword expressions in clinical terminology using definition-based representation learning, achieving strong identification ability with the BioLORD model compared to other state-of-the-art models like SapBERT and CODER.
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.