Cross-Lingual Entity Linking Using GPT Models in Radiology Abstracts

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2025

Autores

Dias, M (Autor) Outra; Carla Teixeira Lopes (Autor) FEUP

Ata de Conferência Internacional

Título: RESEARCH CHALLENGES IN INFORMATION SCIENCE, RCIS 2025, PT II

Páginas: 20-37

19th International Conference on Research Challenges in Information Science-RCIS-Annual

Sevilla, SPAIN, MAY 20-23, 2025

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-018-WAD

DOI: 10.1007/978-3-031-92471-2_2

Abstract (EN): Entity linking is an important task in medical natural language processing (NLP) for converting unstructured text into structured data for clinical analysis and semantic interoperability. However, in lower-resource languages, this task is challenging due to the limited availability of domain-specific resources. This paper explores a translation-based cross-lingual entity linking approach using GPT models, GPT-3.5 and GPT-4o, for zero-shot machine translation and entity linking with in-context learning. We evaluate our approach using a Portuguese-English parallel dataset of radiology abstracts. Our results show that chunk-level machine translation outperforms sentence-level translation. Moreover, our translationbased approach to cross-lingual entity linking of UMLS concepts outperformed the multilingual encoder method baseline. However, the in-context learning entity linking approach did not outperform a translation-based approach with a dictionary-based entity linking method.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 18