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README.md

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-# Event-Extraction
+# Event-Extraction（事件抽取资料综述总结）更新中...
 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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-# Text Classification papers/surveys（文本分类资料综述总结）更新中...
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-This repository contains resources for Natural Language Processing (NLP) with a focus on the task of Text Classification. The content is mainly from paper 《A Survey on Text Classification: From Shallow to Deep Learning》
-（该repository主要总结自然语言处理（NLP）中文本分类任务的资料。内容主要来自文本分类综述论文《A Survey on Text Classification: From Shallow to Deep Learning》）
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 # Table of Contents 目录
 
 - [Surveys（综述论文）](#Surveys)
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-<a href="https://arxiv.org/pdf/2008.00364.pdf">A Survey on Text Classification: From Shallow to Deep Learning（文本分类综述：从浅层模型到深度模型）,2020</a> by<i> Qian Li, Hao Peng, Jianxin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S. Yu, Lifang He
+<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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-Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state of the art approaches from 1961 to 2020, focusing on models from shallow to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area.
-  文本分类是自然语言处理中最基本的任务。由于深度学习的空前成功，过去十年中该领域的研究激增。已有的文献提出了许多方法，数据集和评估指标，从而需要对这些内容进行全面的总结。本文回顾1961年至2020年的文本分类方法，重点是从浅层学习到深度学习的模型。根据所涉及的文本以及用于特征提取和分类的模型创建用于文本分类的分类法。然后，详细讨论这些类别中的每一个类别，涉及支持预测测试的技术发展和基准数据集。并提供了不同技术之间的全面比较，确定了各种评估指标的优缺点。最后，通过总结关键含义，未来的研究方向以及研究领域面临的挑战进行总结。
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-![image](https://github.com/xiaoqian19940510/text-classification-surveys/blob/master/picture1.png)
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-![image](https://github.com/xiaoqian19940510/text-classification-surveys/blob/master/picture2.png)
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+事件抽取是信息抽取领域的一个重要研究方向,在情报收集、知识提取、文档摘要、知识问答等领域有着广泛应用。对当前事件抽取领域研究得较多的元事件抽取进行了综述。首先,简要介绍了元事件和元事件抽取的基本概念,以及元事件抽取的主要实现方法。然后,重点阐述了元事件抽取的主要任务,详细介绍了元事件检测过程,并对其他相关任务进行了概述。最后,总结了元事件抽取面临的问题,在此基础上展望了元事件抽取的发展趋势。
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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:
+</a></summary><blockquote><p align="justify">
+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.
+</p></blockquote></details>
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 ## Deep Learning Models（深度学习模型）
 [:arrow_up:](#table-of-contents)