Detecting Idiomatic Multiword Expressions in Clinical Terminology using
  Definition-Based Representation Learning
François Remy Alfiya Khabibullina Thomas Demeester
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.