This paper presents the Annif system in SemEval-2025 Task 5 (LLMs4Subjects), which focussed on subject indexing using large language models (LLMs). The task required creating subject predictions for bibliographic records from the bilingual TIBKAT database using the GND subject vocabulary. Our approach combines traditional natural language processing and machine learning techniques implemented in the Annif toolkit with innovative LLM-based methods for translation and synthetic data generation, and merging predictions from monolingual models. The system ranked first in the all-subjects category and second in the tib-core-subjects category in the quantitative evaluation, and fourth in qualitative evaluations. These findings demonstrate the potential of combining traditional XMTC algorithms with modern LLM techniques to improve the accuracy and efficiency of subject indexing in multilingual contexts.

本研究解决了多语言背景下的主题索引问题，利用大语言模型（LLMs）增强传统自然语言处理和机器学习技术。通过将翻译、合成数据生成和单语模型预测结合，我们的系统在定量评估中位列所有主题类别第一，并在质性评估中表现出显著的优势，表明传统XMTC算法与现代LLM技术结合的潜力。

Annif在SemEval-2025任务5中的应用：传统的XMTC与大语言模型相结合