Cross-Lingual Entity Linking Using GPT Models in Radiology Abstracts

• Type: Article in International Conference Proceedings Book
• Date: 2025

Authors

Dias, M (Author) Other; Carla Teixeira Lopes (Author) FEUP

Conference proceedings International

Title: Research Challenges in Information Science - 19th International Conference, RCIS 2025, Seville, Spain, May 20-23, 2025, Proceedings, Part II

Pages: 20-37

19th International Conference on Research Challenges in Information Science, RCIS 2025

Seville, 20 May 2025 through 23 May 2025

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: 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 translation-based 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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17