用两阶段检索提升文献主题标签推荐准确率
TartuNLP at SemEval-2025 Task 5: Subject Tagging as Two-Stage Information Retrieval
- 先粗后精：双编码器分步筛选主题标签
- 召回率显著优于单阶段方法
- 适合需要精准分类的图书馆员使用
我们提交了SemEval-2025任务5的方案，旨在帮助图书馆员为文献记录分配主题标签。将该任务建模为信息检索问题：利用文档内容从大型主题分类体系中检索出相关标签。采用两种编码器模型构建两阶段检索系统——第一阶段使用双编码器进行粗粒度候选标签提取，第二阶段使用交叉编码器进行细粒度重排序。该方法有效提升了召回率，相比单阶段方法有显著改进，且在定性评估中表现具有竞争力。
原文摘要 · Abstract (English)
We present our submission to the Task 5 of SemEval-2025 that aims to aid librarians in assigning subject tags to the library records by producing a list of likely relevant tags for a given document. We frame the task as an information retrieval problem, where the document content is used to retrieve subject tags from a large subject taxonomy. We leverage two types of encoder models to build a two-stage information retrieval system -- a bi-encoder for coarse-grained candidate extraction at the first stage, and a cross-encoder for fine-grained re-ranking at the second stage. This approach proved effective, demonstrating significant improvements in recall compared to single-stage methods and showing competitive results according to qualitative evaluation.
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