文献类型：期刊文献

英文题名：Two-Stream Modeling for Document-Level Event Argument Extraction Using Contextual Clue and AMR Structures

作者：Song, Yiqing[1];Shang, Xinna[2];Dai, Guiren[1];Li, Wenfa[3];Yu, Zhi[2]

通讯作者：Shang, XN[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[3]Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2026

卷号：2026

期号：1

外文期刊名：IET SOFTWARE

收录：;EI(收录号：20261020232822);WOS:【SCI-EXPANDED(收录号:WOS:001705089300001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant 62102226) and the Academic Research Projects of Beijing Union University (Grant ZK10202305).

语种：英文

外文关键词：document-level event argument extraction (DEAE); event argument extraction; information extraction; information systems

摘要：Document-level (Doc-level) event argument extraction (EAE) needs to deal with longer text inputs and complex semantic relationships than sentence-level, making it a challenging information extraction task. Extracting event arguments from an entire document primarily faces two critical issues: (i) how to handle the long-distance dependency between trigger and role arguments and (ii) how to extract key event contextual information. We propose a two-stream modeling framework using contextual clues and abstract meaning representation (AMR) parsing (TSCA). TSCA employs two-stream encoding to semantically model the document from event-critical context and event-semantic structure two perspectives. This approach leverages both contextual clues and semantic structure information to better mitigate the two issues. We incorporate AMR to assist in the semantic understanding of complex event structures and effectively capture long-distance dependencies. Additionally, we introduce a span indicator based on triggers to adaptively merge the two-stream information, enhancing the capture of semantic relevance between triggers and candidate arguments. We validated the effectiveness of our method on the public datasets RAMs and Wikievents, where TSCA achieved the best scores in various subtasks, surpassing state-of-the-art models by 3.02 F1 and 1.01 F1, respectively.