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作 者:丁远远 张顺香[1,2,3] 文华 焦熠璇 张基旭 曹宇轩 DING Yuanyuan;ZHANG Shunxiang;WEN Hua;JIAO Yixuan;ZHANG Jixu;CAO Yuxuan(School of Computer Science and Engineering,Anhui University of Science and Technology,Huainan 232001,China;School of Computer,Huainan Normal University,Huainan 232038,China;Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei 230026,China)
机构地区:[1]安徽理工大学计算机科学与工程学院,淮南232001 [2]淮南师范学院计算机学院,淮南232038 [3]合肥综合性国家科学中心人工智能研究院,合肥230026
出 处:《数据采集与处理》2025年第2期517-529,共13页Journal of Data Acquisition and Processing
基 金:国家自然科学基金(62076006);认知智能全国重点实验室开放课题(COGOS-2023HE02);安徽高校协同创新项目(GXXT-2021-008)。
摘 要:针对现有研究将事件检测过程分解为触发词识别和分类两个阶段性任务,从而引发误差传递的问题,本文提出一种基于类型语义提示的事件检测方法。通过将事件类型作为提示信息来引导模型从事件文本中抽取与事件类型对应的触发词,从而并行执行触发词的识别和分类,缓解任务间误差传递的问题。首先利用跨注意力机制处理事件文本表征和事件类型提示模板,获得融合事件文本信息的提示表征;然后计算提示表征与事件文本表征间的余弦相似度,得到与事件类型对应的触发词在事件文本中位置的概率分布;最后基于位置的概率分布确定触发词的位置,从而同时实现触发词的识别与分类。在ACE2005和MACCROBAT⁃EE数据集上的实验结果表明,本文方法在事件检测任务中的F1值均有提升。Addressing the issue of error propagation in existing research that decomposes the event detection process into two staged tasks of trigger recognition and classification,this paper proposes an event detection method based on type⁃semantic prompts.This method uses event types as prompt information to guide the model in extracting triggers corresponding to the event types from event text.It enables the parallel execution of trigger recognition and classification tasks,thereby mitigating the issue of error propagation between tasks.Firstly,the cross⁃attention mechanism is utilized to process the representation of the event text and the prompt template consisting of event types,obtaining a fused prompt representation that integrates the event text information.Then,the cosine similarity between the prompt representation and the event context representation is computed to obtain the probability distribution of the trigger positions corresponding to the event types in the event text.Finally,the position of the trigger corresponding to the event type is determined based on the probability distribution of positions,thus achieving parallel execution of trigger recognition and classification tasks.Experimental results on the ACE2005 and MACCROBAT⁃EE datasets demonstrate an improvement in the F1 score of the proposed method in event detection tasks.
关 键 词:事件检测 提示学习 跨注意力机制 余弦相似度 概率分布
分 类 号:TP391.1[自动化与计算机技术—计算机应用技术]
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