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# Event-Extraction（事件抽取资料综述总结）更新中...
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近年来事件抽取方法总结，包括中文事件抽取、开放域事件抽取、事件数据生成、跨语言事件抽取、小样本事件抽取、零样本事件抽取等类型，DMCNN、FramNet、DLRNN、DBRNN、GCN、DAG-GRU、JMEE、PLMEE等方法
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##事件抽取的定义
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（1） Closed-domain
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Closed-domain event extraction uses predefined event schema to discover and extract desired events of particular type from text. An event schema contains several event types and their corresponding event structures. We use the ACE terminologies to introduce an event structure as follows:
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Event mention:a phrase or sentence describing an event, including a trigger and several arguments.
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Event trigger:the main word that most clearly expresses an event occurrence, typically a verb or a noun.
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Event argument:an entity mention, temporal expression or value that serves as a participant or attribute with a specific role in an event.
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Argument role:the relationship between an argument to the event in which it participants.
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D.Ahn [the stages of event extraction] first proposed to divide the ACE event extraction task into four subtasks: trigger detection, event/trigger type identification, event argument detection, and argument role identification.
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Event mention: a phrase or sentence within which an event is described, including a trigger and arguments.
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Event trigger: the main word that most clearly expresses the occurrence of an event (An ACE event trigger is typically a verb or a noun).
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Event argument: an entity mention, temporal expression or value (e.g. Job-Title) that is involved in an event (viz., participants).
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Argument role: the relationship between an argument to the event in which it participates.
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（2） Open domain
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Without predefined event schemas, open-domain event extraction aims at detecting events from texts and in most cases, also clustering similar events via extracted event key-words. Event keywords refer to those words/phrases mostly describing an event, and sometimes keywords are further divided into triggers and arguments.
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Story segmentation: detecting the boundaries of a story from news articles.
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First story detection: detecting the story that discuss anew topic in the stream of news.
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Topic detection: grouping the stories based on the topics they discuss.
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Topic tracking: detecting stories that discuss a previously known topic.
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Story link detection: deciding whether a pair of stories discuss the same topic.
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The first two tasks mainly focus on event detection; and the rest three tasks are for event clustering. While the relation between the five tasks is evident, each requires a distinct evaluation process and encourages different approaches to address the particular problem.
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# Table of Contents 目录
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---
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1 Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection,WSDM 2020.FewEvent数据集链接: https://github.com/231sm/Low_Resource_KBP
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2 Exploring Pre-trained Language Models for Event Extraction and Generation，ACL2019
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3 One for All: Neural Joint Modeling of Entities and Events,AAAI2019
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4 Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction, EMNLP2019
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5 Zero-Shot Transfer Learning for Event Extraction [HuangL,ACL2018]
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6 Joint Event Extraction via Recurrent Neural Networks, NAACL-HLT 2016
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7 Joint Entity and Event Extraction with Generative Adversarial Imitation Learning[T Zhang, H Ji, etc.]
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8 Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks[Y Chen, 2015]
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9 Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction,一种针对中文金融事件抽取的端到端文档
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综述论文：
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1 An Overview of Event Extraction from Text [F Hogenboom .etc ]
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2 元事件抽取研究综述 (Survey on Meta-event Extraction).
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## Surveys(综述论文)
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[:arrow_up:](#table-of-contents)
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### 事件抽取综述
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<details/>
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<summary/>
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<a href="https://arxiv.org/pdf/2008.00364.pdf">元事件抽取研究综述，2019</a> by<i>GAO Li-zheng, ZHOU Gang, LUO Jun-yong, LAN Ming-jing
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<details/>
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<summary/>
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<a href="https://arxiv.org/pdf/2008.00364.pdf">An Overview of Event Extraction from Text，2011</a> by<i>Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong:
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<a href="https://arxiv.org/pdf/2008.00364.pdf">An Overview of Event Extraction from Text，2019</a> by<i>Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong:
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</a></summary><blockquote><p align="justify">
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One common application of text mining is event extraction,which encompasses deducing specific knowledge concerning incidents re-ferred to in texts. Event extraction can be applied to various types ofwritten text, e.g., (online) news messages, blogs, and manuscripts. Thisliterature survey reviews text mining techniques that are employed forvarious event extraction purposes. It provides general guidelines on howto choose a particular event extraction technique depending on the user,the available content, and the scenario of use.
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</p></blockquote></details>
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<details/>
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<summary/>
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<a href="https://arxiv.org/pdf/2008.00364.pdf">A Survey of Event Extraction from Text，2019</a> by<i>Wei Xiang, Bang Wang </a></summary><blockquote><p align="justify">
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事件提取的任务定义、数据源和性能评估，还为其解决方案方法提供了分类。在每个解决方案组中，提供了最具代表性的方法的详细分析，特别是它们的起源、基础、优势和弱点。最后，对未来的研究方向进行了展望。
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</p></blockquote></details>
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<details/>
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<summary/>
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<a href="https://arxiv.org/pdf/2008.00364.pdf">A Survey of Textual Event Extraction from Social Networks，2017</a> by<i>Mohamed Mejri, Jalel Akaichi </a></summary><blockquote><p align="justify">
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。
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</p></blockquote></details>
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<details/>
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<summary/>
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<a href="https://arxiv.org/pdf/2008.00364.pdf">A Survey of event extraction methods from text for decision support systems，2016</a> by<i>Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong, Emiel Caron </a></summary><blockquote><p align="justify">
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。
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</p></blockquote></details>
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<details/>
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<summary/>
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<a href="https://arxiv.org/pdf/2008.00364.pdf">A Survey of Textual Event Extraction from Social Networks，2014</a> by<i>Vera DanilovaMikhail AlexandrovXavier Blanco </a></summary><blockquote><p align="justify">
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。
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</p></blockquote></details>
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### 事件检测综述
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<details/>
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<summary/>
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<a href="https://arxiv.org/pdf/2008.00364.pdf">A survey on multi-modal social event detection，2020</a> by<i>Han Zhou, Hongpeng Yin, Hengyi Zheng, Yanxia Li</a></summary><blockquote><p align="justify">
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回顾了事件特征学习和事件推理这两种研究方法。特别是，学习事件特征是必要的前提，因为它能够将社交媒体数据转换为计算机友好的数字形式。事件推理的目的是决定一个样本是否属于一个社会事件。然后，介绍了该社区的几个公共数据集，并给出了比较结果。在本文的最后，本文对如何促进多模式社会事件检测的发展进行了一般性的讨论。
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</p></blockquote></details>
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<details/>
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<summary/>
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<a href="https://arxiv.org/pdf/2008.00364.pdf">Review on event detection techniques in social multimedia，2016</a> by<i>Han Zhou, Hongpeng Yin, Hengyi Zheng, Yanxia Li</a></summary><blockquote><p align="justify">
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。
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</p></blockquote></details>
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## Deep Learning Models（深度学习模型）
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[:arrow_up:](#table-of-contents)
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