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.
@article{arxiv.2504.21547,
title = {TartuNLP at SemEval-2025 Task 5: Subject Tagging as Two-Stage Information Retrieval},
author = {Aleksei Dorkin and Kairit Sirts},
journal= {arXiv preprint arXiv:2504.21547},
year = {2025}
}
Comments
To appear in the Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)