@inproceedings{singh-etal-2025-silp-nlp,
title = "silp{\_}nlp at {S}em{E}val-2025 Task 5: Subject Recommendation With Sentence Transformer",
author = "Singh, Sumit and
Goyal, Pankaj and
Tiwary, Uma",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.320/",
pages = "2455--2460",
ISBN = "979-8-89176-273-2",
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."
}
Markdown (Informal)
[silp_nlp at SemEval-2025 Task 5: Subject Recommendation With Sentence Transformer](https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.320/) (Singh et al., SemEval 2025)
ACL