@inproceedings{liang-etal-2025-adaptive,
title = "Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation",
author = "Liang, Sheng and
Lv, Hang and
Wen, Zhihao and
Wu, Yaxiong and
Zhang, Yongyue and
Wang, Hao and
Liu, Yong",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.419/",
pages = "7927--7946",
ISBN = "979-8-89176-335-7",
abstract = "Event extraction (EE) is a fundamental task in natural language processing (NLP) that involves identifying and extracting event information from unstructured text. Effective EE in real-world scenarios requires two key steps: selecting appropriate schemas from hundreds of candidates and executing the extraction process.Existing research exhibits two critical gaps: (1) the rigid schema fixation in existing pipeline systems, and (2) the absence of benchmarks for evaluating joint schema matching and extraction.Although large language models (LLMs) offer potential solutions, their schema hallucination tendencies and context window limitations pose challenges for practical deployment. In response, we propose $\textbf{A}$daptive $\textbf{S}$chema-aware $\textbf{E}$vent $\textbf{E}$xtraction ($\textbf{ASEE}$), a novel paradigm combining schema paraphrasing with schema retrieval-augmented generation. ASEE adeptly retrieves paraphrased schemas and accurately generates targeted structures.To facilitate rigorous evaluation, we construct the $\textbf{M}$ulti-$\textbf{D}$imensional $\textbf{S}$chema-aware $\textbf{E}$vent $\textbf{E}$xtraction ($\textbf{MD-SEE}$) benchmark, which systematically consolidates 12 datasets across diverse domains, complexity levels, and language settings.Extensive evaluations on MD-SEE show that our proposed ASEE demonstrates strong adaptability across various scenarios, significantly improving the accuracy of event extraction. Our codes and datasets are available at https://github.com/USTC-StarTeam/ASEE.git"
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<abstract>Event extraction (EE) is a fundamental task in natural language processing (NLP) that involves identifying and extracting event information from unstructured text. Effective EE in real-world scenarios requires two key steps: selecting appropriate schemas from hundreds of candidates and executing the extraction process.Existing research exhibits two critical gaps: (1) the rigid schema fixation in existing pipeline systems, and (2) the absence of benchmarks for evaluating joint schema matching and extraction.Although large language models (LLMs) offer potential solutions, their schema hallucination tendencies and context window limitations pose challenges for practical deployment. In response, we propose Adaptive Schema-aware Event Extraction (ASEE), a novel paradigm combining schema paraphrasing with schema retrieval-augmented generation. ASEE adeptly retrieves paraphrased schemas and accurately generates targeted structures.To facilitate rigorous evaluation, we construct the Multi-Dimensional Schema-aware Event Extraction (MD-SEE) benchmark, which systematically consolidates 12 datasets across diverse domains, complexity levels, and language settings.Extensive evaluations on MD-SEE show that our proposed ASEE demonstrates strong adaptability across various scenarios, significantly improving the accuracy of event extraction. Our codes and datasets are available at https://github.com/USTC-StarTeam/ASEE.git</abstract>
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%0 Conference Proceedings
%T Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation
%A Liang, Sheng
%A Lv, Hang
%A Wen, Zhihao
%A Wu, Yaxiong
%A Zhang, Yongyue
%A Wang, Hao
%A Liu, Yong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F liang-etal-2025-adaptive
%X Event extraction (EE) is a fundamental task in natural language processing (NLP) that involves identifying and extracting event information from unstructured text. Effective EE in real-world scenarios requires two key steps: selecting appropriate schemas from hundreds of candidates and executing the extraction process.Existing research exhibits two critical gaps: (1) the rigid schema fixation in existing pipeline systems, and (2) the absence of benchmarks for evaluating joint schema matching and extraction.Although large language models (LLMs) offer potential solutions, their schema hallucination tendencies and context window limitations pose challenges for practical deployment. In response, we propose Adaptive Schema-aware Event Extraction (ASEE), a novel paradigm combining schema paraphrasing with schema retrieval-augmented generation. ASEE adeptly retrieves paraphrased schemas and accurately generates targeted structures.To facilitate rigorous evaluation, we construct the Multi-Dimensional Schema-aware Event Extraction (MD-SEE) benchmark, which systematically consolidates 12 datasets across diverse domains, complexity levels, and language settings.Extensive evaluations on MD-SEE show that our proposed ASEE demonstrates strong adaptability across various scenarios, significantly improving the accuracy of event extraction. Our codes and datasets are available at https://github.com/USTC-StarTeam/ASEE.git
%U https://aclanthology.org/2025.findings-emnlp.419/
%P 7927-7946
Markdown (Informal)
[Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation](https://aclanthology.org/2025.findings-emnlp.419/) (Liang et al., Findings 2025)
ACL