@inproceedings{zeng-etal-2025-codetaxo,
title = "{C}ode{T}axo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts",
author = "Zeng, Qingkai and
Bai, Yuyang and
Tan, Zhaoxuan and
Wu, Zhenyu and
Feng, Shangbin and
Jiang, Meng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.214/",
doi = "10.18653/v1/2025.findings-acl.214",
pages = "4131--4144",
ISBN = "979-8-89176-256-5",
abstract = "Taxonomies provide structural representations of knowledge and are crucial in various applications. The task of taxonomy expansion involves integrating emerging entities into existing taxonomies by identifying appropriate parent entities for these new query entities. Previous methods rely on self-supervised techniques that generate annotation data from existing taxonomies but are less effective with small taxonomies (fewer than 100 entities). In this work, we introduce CodeTaxo, a novel approach that leverages large language models through code language prompts to capture the taxonomic structure. Extensive experiments on five real-world benchmarks from different domains demonstrate that CodeTaxo consistently achieves superior performance across all evaluation metrics, significantly outperforming previous state-of-the-art methods. The code and data are available at \url{https://github.com/QingkaiZeng/CodeTaxo-official}."
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<abstract>Taxonomies provide structural representations of knowledge and are crucial in various applications. The task of taxonomy expansion involves integrating emerging entities into existing taxonomies by identifying appropriate parent entities for these new query entities. Previous methods rely on self-supervised techniques that generate annotation data from existing taxonomies but are less effective with small taxonomies (fewer than 100 entities). In this work, we introduce CodeTaxo, a novel approach that leverages large language models through code language prompts to capture the taxonomic structure. Extensive experiments on five real-world benchmarks from different domains demonstrate that CodeTaxo consistently achieves superior performance across all evaluation metrics, significantly outperforming previous state-of-the-art methods. The code and data are available at https://github.com/QingkaiZeng/CodeTaxo-official.</abstract>
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%0 Conference Proceedings
%T CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts
%A Zeng, Qingkai
%A Bai, Yuyang
%A Tan, Zhaoxuan
%A Wu, Zhenyu
%A Feng, Shangbin
%A Jiang, Meng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zeng-etal-2025-codetaxo
%X Taxonomies provide structural representations of knowledge and are crucial in various applications. The task of taxonomy expansion involves integrating emerging entities into existing taxonomies by identifying appropriate parent entities for these new query entities. Previous methods rely on self-supervised techniques that generate annotation data from existing taxonomies but are less effective with small taxonomies (fewer than 100 entities). In this work, we introduce CodeTaxo, a novel approach that leverages large language models through code language prompts to capture the taxonomic structure. Extensive experiments on five real-world benchmarks from different domains demonstrate that CodeTaxo consistently achieves superior performance across all evaluation metrics, significantly outperforming previous state-of-the-art methods. The code and data are available at https://github.com/QingkaiZeng/CodeTaxo-official.
%R 10.18653/v1/2025.findings-acl.214
%U https://aclanthology.org/2025.findings-acl.214/
%U https://doi.org/10.18653/v1/2025.findings-acl.214
%P 4131-4144
Markdown (Informal)
[CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts](https://aclanthology.org/2025.findings-acl.214/) (Zeng et al., Findings 2025)
ACL