@inproceedings{salfinger-etal-2025-la2i2f,
title = "{LA}{\texttwosuperior}{I}{\texttwosuperior}{F} at {S}em{E}val-2025 Task 5: Reasoning in Embedding Space {--} Fusing Analogical and Ontology-based Reasoning for Document Subject Tagging",
author = "Salfinger, Andrea and
Zaccagna, Luca and
Incitti, Francesca and
De Nardi, Gianluca and
Dal Fabbro, Lorenzo and
Snidaro, Lauro",
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://aclanthology.org/2025.semeval-1.314/",
pages = "2413--2423",
ISBN = "979-8-89176-273-2",
abstract = "The LLMs4Subjects shared task invited system contributions that leverage a technical library{'}s tagged document corpus to learn document subject tagging, i.e., proposing adequate subjects given a document{'}s title and abstract. To address the imbalance of this training corpus, team LA{\texttwosuperior}I{\texttwosuperior}F devised a semantic retrieval-based system fusing the results of ontological and analogical reasoning in embedding vector space. Our results outperformed a naive baseline of prompting a llama 3.1-based model, whilst being computationally more efficient and competitive with the state of the art."
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<abstract>The LLMs4Subjects shared task invited system contributions that leverage a technical library’s tagged document corpus to learn document subject tagging, i.e., proposing adequate subjects given a document’s title and abstract. To address the imbalance of this training corpus, team LA²I²F devised a semantic retrieval-based system fusing the results of ontological and analogical reasoning in embedding vector space. Our results outperformed a naive baseline of prompting a llama 3.1-based model, whilst being computationally more efficient and competitive with the state of the art.</abstract>
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%0 Conference Proceedings
%T LA²I²F at SemEval-2025 Task 5: Reasoning in Embedding Space – Fusing Analogical and Ontology-based Reasoning for Document Subject Tagging
%A Salfinger, Andrea
%A Zaccagna, Luca
%A Incitti, Francesca
%A De Nardi, Gianluca
%A Dal Fabbro, Lorenzo
%A Snidaro, Lauro
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F salfinger-etal-2025-la2i2f
%X The LLMs4Subjects shared task invited system contributions that leverage a technical library’s tagged document corpus to learn document subject tagging, i.e., proposing adequate subjects given a document’s title and abstract. To address the imbalance of this training corpus, team LA²I²F devised a semantic retrieval-based system fusing the results of ontological and analogical reasoning in embedding vector space. Our results outperformed a naive baseline of prompting a llama 3.1-based model, whilst being computationally more efficient and competitive with the state of the art.
%U https://aclanthology.org/2025.semeval-1.314/
%P 2413-2423
Markdown (Informal)
[LA²I²F at SemEval-2025 Task 5: Reasoning in Embedding Space – Fusing Analogical and Ontology-based Reasoning for Document Subject Tagging](https://aclanthology.org/2025.semeval-1.314/) (Salfinger et al., SemEval 2025)
ACL