silp_nlp at S em E val-2025 Task 5: Subject Recommendation With Sentence Transformer (2025)

Abstract: This work explored subject recommendation using sentence transformers within the SemEval-2025 Task 5 (LLMs4Subjects) challenge. Our approach leveraged embedding-based cosine similarity and hierarchical clustering to predict relevant GND subjects for TIB technical records in English and German. By experimenting with different models, including JinaAi, Distiluse-base-multilingual, and TF-IDF, we found that the JinaAi sentence transformer consistently outperformed other methods in terms of precision, recall, and F1-score.Our results highlight the effectiveness of transformer-based embeddings in semantic similarity tasks for subject classification. Additionally, hierarchical clustering helped reduce computational complexity by narrowing down candidate subjects efficiently. Despite the improvements, future work can focus on fine-tuning domain-specific embeddings, exploring knowledge graph integration, and enhancing multilingual capabilities for better generalization.

## introduction the llms4subjects shared task @xcite aims to develop a subject recommendation system. the goal is to predict the most relevant subjects from the entire gnd 1 subject collection to tag a given tib technical record 2 . each system will receive a technical record's title and abstract as input, and it must generate a customizable top-k list of relevant gnd subjects. since the input records may be in english or german, the systems should support bilingual semantic processing. this task explores the potential of language model solutions for subject classification and tagging. it is based on the open-access tib collection, specifically tibkat, which includes over 100,000 records such as technical reports, publications, and books, primarily in english and german. these records are classified according to the gnd subjects taxonomy. leveraging statistical methods and transformerbased architectures for subject classification offers significant advantages. semantic indexing with other vocabularies has gained attraction @xcite 1 gnd 2 tib technical record 2021; @xcite . notably, the prediction of medical subject headings (mesh) for biomedical literature has experienced significant advancements through the application of deep learning and machine learning techniques @xcite . in recent years, transformer-based models have demonstrated remarkable success across various natural language processing (nlp) tasks. among them, sentence transformers have attracted significant attention, particularly for tasks involving sentence similarity measurement. in this work, we employed sentence transformers to compute sentence similarity, leveraging cosine similarity as the distance metric. the datasets provided for the task encompass five distinct types of documents: articles, books, conference papers, reports, and theses. ## datasets as part of the shared task, we were provided with datasets (d'souza et al., 2024) containing two versions of the gnd taxonomy: ## results & analysis similarly, ## conclusion this work explored subject recommendation using sentence transformers within the semeval-2025 task 5 (llms4subjects) challenge. our approach leveraged embedding-based cosine similarity and hierarchical clustering to predict relevant gnd subjects for tib technical records in english and german. by experimenting with different models, including jinaai, distiluse-base-multilingual, and tf-idf, we found that the jinaai sentence transformer consistently outperformed other methods in terms of precision, recall, and f1-score. our results highlight the effectiveness of transformer-based embeddings in semantic similarity tasks for subject classification. additionally, hierarchical clustering helped reduce computational complexity by narrowing down candidate subjects efficiently. despite the improvements, future work can focus on fine-tuning domain-specific embeddings, exploring knowledge graph integration, and enhancing multilingual capabilities for better generalization.