Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918128454205440 |
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| author | Suominen, Osma Inkinen, Juho Lehtinen, Mona |
| author_facet | Suominen, Osma Inkinen, Juho Lehtinen, Mona |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_19675 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs Suominen, Osma Inkinen, Juho Lehtinen, Mona Computation and Language Artificial Intelligence Digital Libraries Information Retrieval Machine Learning I.2.7 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. |
| title | Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs |
| topic | Computation and Language Artificial Intelligence Digital Libraries Information Retrieval Machine Learning I.2.7 |
| url | https://arxiv.org/abs/2504.19675 |