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.
Abstract (translated)
这篇文章强调了基于定义语义模型在检测临床术语中的idiomatic和半idiomatic多字表达式(MWEs)的潜力。我们的研究关注于UMLS ontology中定义的生物医学实体，并旨在帮助对这些实体的翻译工作进行优先排序。特别是，我们开发了一种有效的工具，用于根据那些MWEs的语义表示和对其构成成分的加权平均表示之间的相似程度评分它们的idiomaticity。我们使用了一个训练用于产生实体名称和定义相似的表示的生物医学语言模型，称为BioLORD，并将它与其他基于Transformer的最先进的生物医学语言模型——Sapien BERT和CodeR进行比较。我们将这种基于定义的方法的重要性强调了，通过比较BioLORD模型与其他模型的性能，例如基于Transformer的Sapien BERT和CodeR。我们的结果表明，BioLORD模型具有很强的idiomatic识别能力，而其他模型并没有复制这种能力。我们的无语料库idiomaticity估计帮助Ontology翻译人员专注于更困难的MWEs。
URL
https://arxiv.org/abs/2305.06801
