EriBERTa：面向临床自然语言处理的双语预训练语言模型
EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing
AI总结：
针对西班牙语临床领域专用语言模型匮乏的问题，本文提出在大规模医疗临床语料上预训练的双语模型EriBERTa，其临床任务表现优于现有西语模型，且跨语言迁移能力可缓解西语临床数据不足的困境。
摘要：
将临床报告用于健康研究、治疗监测等各类二次用途，对提升患者护理水平至关重要。自然语言处理（NLP）工具已成为从这类报告中提取、处理相关信息的重要手段，但目前西班牙语临床领域专用语言模型的可用资源十分有限。\n本文提出EriBERTa——一个在大规模医疗与临床语料库上预训练的双语领域专用语言模型。实验表明，EriBERTa在临床领域的表现优于现有西班牙语语言模型，展现出更强的医学文本理解与关键信息提取能力。此外，EriBERTa具备良好的迁移学习能力，可实现跨语言知识迁移，这一特性在西班牙语临床数据稀缺的背景下尤为实用。
英文摘要：
The utilization of clinical reports for various secondary purposes, including health research and treatment monitoring, is crucial for enhancing patient care. Natural Language Processing (NLP) tools have emerged as valuable assets for extracting and processing relevant information from these reports. However, the availability of specialized language models for the clinical domain in Spanish has been limited. In this paper, we introduce EriBERTa, a bilingual domain-specific language model pre-trained on extensive medical and clinical corpora. We demonstrate that EriBERTa outperforms previous Spanish language models in the clinical domain, showcasing its superior capabilities in understanding medical texts and extracting meaningful information. Moreover, EriBERTa exhibits promising transfer learning abilities, allowing for knowledge transfer from one language to another. This aspect is particularly beneficial given the scarcity of Spanish clinical data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
