| 摘要: |
| 事件抽取是从非结构化的自然语言文本中自动抽取用户感兴趣的事件信息, 并以结构化的形式表示出来. 事件抽取是自然语言处理与理解中的重要方向, 在政府公共事务管理、金融业务、生物医学等不同领域有着很高的应用价值. 根据对人工标注数据的依赖程度, 目前基于深度学习的事件抽取方法主要分为两类: 有监督和远程监督学习方法. 对当前深度学习中事件抽取技术进行了全面的综述. 围绕有监督中CNN、RNN、GAN、GCN与远程监督等方法, 系统地总结了近几年的研究情况, 并对不同的深度学习模型的性能进行了详细对比与分析. 最后, 对事件抽取面临的挑战进行了分析, 针对研究趋势进行了展望. |
| 关键词: 事件抽取 有监督学习 深度学习 远程监督 信息抽取 |
| DOI：10.13328/j.cnki.jos.006645 |
| 分类号: |
| 基金项目:国家自然科学基金(61402099,61702093) |
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| Survey on Event Extraction Based on Deep Learning |
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WANG Hao-Chang1, ZHOU Chen-Lian1, Marius Gabriel PETRESCU2
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1.School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China;2.Universitatea Petrol-gaze din Ploiesti, Bucharest 100680, Romania
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| Abstract: |
| Event extraction is to automatically extract event information in which users are interested from unstructured natural language texts and express it in a structured form. Event extraction is an important direction in natural language processing and understanding and is of high application value in different fields, such as government management of public affairs, financial business, and biomedicine. According to the degree of dependence on manually labeled data, the current event extraction methods based on deep learning are mainly divided into two categories: supervised learning and distantly-supervised learning. This article provides a comprehensive overview of current event extraction techniques in deep learning. Focusing on supervised methods such as CNN, RNN, GAN, GCN, and distant supervision, this study systematically summarizes the research in recent years. Additionally, the performance of different deep learning models is compared and analyzed in detail. Finally, the challenges facing event extraction are analyzed, and the research trends are forecasted. |
| Key words: event extraction supervised learning deep learning distant supervision information extraction |
