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        <title>Document-Level Zero-Shot Relation Extraction with Entity Side Information</title>
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        <namePart type="given">Mohan</namePart>
        <namePart type="family">Raj</namePart>
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        <namePart type="given">Lay-Ki</namePart>
        <namePart type="family">Soon</namePart>
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        <namePart type="family">Ong</namePart>
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    <name type="personal">
        <namePart type="given">Bhawani</namePart>
        <namePart type="family">Selvaretnam</namePart>
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            <title>Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Vera</namePart>
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    <abstract>Document-Level Zero-Shot Relation Extraction (DocZSRE) aims to predict unseen relation labels in text documents without prior training on specific relations. Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels, which poses challenges for low-resource languages like Malaysian English. These challenges include the incorporation of local linguistic nuances and the risk of factual inaccuracies in LLM-generated data. This paper introduces Document-Level Zero-Shot Relation Extraction with Entity Side Information (DocZSRE-SI) to address limitations in the existing DocZSRE approach. The DocZSRE-SI framework leverages Entity Side Information, such as Entity Mention Descriptions and Entity Mention Hypernyms, to perform ZSRE without depending on LLM-generated synthetic data. The proposed low-complexity model achieves an average improvement of 11.6% in the macro F1-Score compared to baseline models and existing benchmarks. By utilising Entity Side Information, DocZSRE-SI offers a robust and efficient alternative to error-prone, LLM-based methods, demonstrating significant advancements in handling low-resource languages and linguistic diversity in relation extraction tasks. This research provides a scalable and reliable solution for ZSRE, particularly in contexts like Malaysian English news articles, where traditional LLM-based approaches fall short.</abstract>
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            <start>4670</start>
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%0 Conference Proceedings
%T Document-Level Zero-Shot Relation Extraction with Entity Side Information
%A Raj, Mohan
%A Soon, Lay-Ki
%A Ong, Huey Fang
%A Selvaretnam, Bhawani
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F raj-etal-2026-document
%X Document-Level Zero-Shot Relation Extraction (DocZSRE) aims to predict unseen relation labels in text documents without prior training on specific relations. Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels, which poses challenges for low-resource languages like Malaysian English. These challenges include the incorporation of local linguistic nuances and the risk of factual inaccuracies in LLM-generated data. This paper introduces Document-Level Zero-Shot Relation Extraction with Entity Side Information (DocZSRE-SI) to address limitations in the existing DocZSRE approach. The DocZSRE-SI framework leverages Entity Side Information, such as Entity Mention Descriptions and Entity Mention Hypernyms, to perform ZSRE without depending on LLM-generated synthetic data. The proposed low-complexity model achieves an average improvement of 11.6% in the macro F1-Score compared to baseline models and existing benchmarks. By utilising Entity Side Information, DocZSRE-SI offers a robust and efficient alternative to error-prone, LLM-based methods, demonstrating significant advancements in handling low-resource languages and linguistic diversity in relation extraction tasks. This research provides a scalable and reliable solution for ZSRE, particularly in contexts like Malaysian English news articles, where traditional LLM-based approaches fall short.
%U https://aclanthology.org/2026.eacl-long.216/
%P 4670-4680

Markdown (Informal)

[Document-Level Zero-Shot Relation Extraction with Entity Side Information](https://aclanthology.org/2026.eacl-long.216/) (Raj et al., EACL 2026)

• Document-Level Zero-Shot Relation Extraction with Entity Side Information (Raj et al., EACL 2026)

ACL

• Mohan Raj, Lay-Ki Soon, Huey Fang Ong, and Bhawani Selvaretnam. 2026. Document-Level Zero-Shot Relation Extraction with Entity Side Information. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4670–4680, Rabat, Morocco. Association for Computational Linguistics.