XMEN: a modular toolkit for cross-lingual medical entity normalization.

ABSTRACT:

Objective

To improve performance of medical entity normalization across many languages, especially when fewer language resources are available compared to English.

Materials and methods

We propose xMEN, a modular system for cross-lingual (x) medical entity normalization (MEN), accommodating both low- and high-resource scenarios. To account for the scarcity of aliases for many target languages and terminologies, we leverage multilingual aliases via cross-lingual candidate generation. For candidate ranking, we incorporate a trainable cross-encoder (CE) model if annotations for the target task are available. To balance the output of general-purpose candidate generators with subsequent trainable re-rankers, we introduce a novel rank regularization term in the loss function for t

SUBMITTER: Borchert F 

PROVIDER: S-EPMC11671143 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature