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README.md
@ -1054,21 +1054,122 @@ Keywords: Semi-supervised
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#### 2019
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<details/>
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<summary/>
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<a href="https://arxiv.org/abs/1907.11692">Neural Cross-Lingual Event Detection with Minimal Parallel Resources, EMNLP2019(<a href="https://github.com/pytorch/fairseq">Github</a>)</summary><blockquote><p align="justify">
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The scarcity in annotated data poses a great challenge for event detection (ED). Cross-lingual ED aims to tackle this challenge by transferring knowledge between different languages to boost performance. However, previous cross-lingual methods for ED demonstrated a heavy dependency on parallel resources, which might limit their applicability. In this paper, we propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources. Specifically, to construct a lexical mapping between different languages, we devise a context-dependent translation method; to treat the word order difference problem, we propose a shared syntactic order event detector for multilingual co-training. The efficiency of our method is studied through extensive experiments on two standard datasets. Empirical results indicate that our method is effective in 1) performing cross-lingual transfer concerning different directions and 2) tackling the extremely annotation-poor scenario.
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<a href="https://link.zhihu.com/?target=https%3A//www.aclweb.org/anthology/D19-1030/" >Cross-lingual Structure Transfer for Relation and Event Extraction, EMNLP 2019) </a> by<i> Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss
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(<a>Github</a>)</summary><blockquote><p align="justify">
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动机：
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从资源不足以及注释不足的语料库中进行复杂语义结构的识别（例如事件和实体关系）是很困难的，这已经变成了一个很有挑战性的信息抽取任务。
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主要思想：
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通过使用卷积神经网络，将所有实体信息片段、事件触发词、事件背景放入一个复杂的、结构化的多语言公共空间，然后我们可以从源语言注释中训练一个事件抽取器，并将它应用于目标语言。
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数据集：ACE2005
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</p></blockquote></details>
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#### 2016
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<details/>
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<summary/>
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<a href="https://link.zhihu.com/?target=https%3A//www.aclweb.org/anthology/C16-1114" >Leveraging Multilingual Training for Limited Resource Event Extraction, COLING 2016 </a> by<i> Hsi, Andrew and Yang, Yiming and Carbonell, Jaime and Xu, Ruochen
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(<a>Github</a>)</summary><blockquote><p align="justify">
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动机：迄今为止，利用跨语言培训来提高性能的工作非常有限。因此我们提出了一种新的事件抽取方法。
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主要思想：在本文中，我们提出了一种新颖的跨语言事件抽取方法，该方法可在多种语言上进行训练，并利用依赖于语言的特征和不依赖于语言的特征来提高性能。使用这种系统，我们旨在同时利用可用的多语言资源（带注释的数据和引入的特征）来克服目标语言中的注释稀缺性问题。 从经验上我们认为，我们的方法可以极大地提高单语系统对中文事件论元提取任务的性能。 与现有工作相比，我们的方法是新颖的，我们不依赖于使用高质量的机器翻译的或手动对齐的文档，这因为这种需求对于给定的目标语言可能是无法满足的。
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数据集：ACE2005
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Keywords: Training on multiple languages using a combination of both language-dependent and language-independent features
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</p></blockquote></details>
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### 数据生成
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#### 2017
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<details/>
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<summary/>
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<a href="https://link.zhihu.com/?target=https%3A//www.aclweb.org/anthology/P17-1038" >Automatically Labeled Data Generation for Large Scale Event Extraction, ACL 2017 </a> by<i> Chen, Yubo and Liu, Shulin and Zhang, Xiang and Liu, Kang and Zhao, Jun
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(<a href="https://link.zhihu.com/?target=https%3A//github.com/acl2017submission/event-data">Github</a>)</summary><blockquote><p align="justify">
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动机：手动标记的训练数据成本太高，事件类型覆盖率低且规模有限，这种监督的方法很难从知识库中抽取大量事件。
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主要思想：1）提出了一种按重要性排列论元并且为每种事件类型选取关键论元或代表论元方法。2）仅仅使用关键论元来标记事件，并找出关键词。3）用外部语言知识库FrameNet来过滤噪声触发词并且扩展触发词库。
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数据集：ACE2005
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Keywords: Data Generation, Distant Supervision
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</p></blockquote></details>
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#### 2019
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<details/>
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<summary/>
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<a href="https://arxiv.org/abs/1907.11692">Neural Cross-Lingual Event Detection with Minimal Parallel Resources, EMNLP2019(<a href="https://github.com/pytorch/fairseq">Github</a>)</summary><blockquote><p align="justify">
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The scarcity in annotated data poses a great challenge for event detection (ED). Cross-lingual ED aims to tackle this challenge by transferring knowledge between different languages to boost performance. However, previous cross-lingual methods for ED demonstrated a heavy dependency on parallel resources, which might limit their applicability. In this paper, we propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources. Specifically, to construct a lexical mapping between different languages, we devise a context-dependent translation method; to treat the word order difference problem, we propose a shared syntactic order event detector for multilingual co-training. The efficiency of our method is studied through extensive experiments on two standard datasets. Empirical results indicate that our method is effective in 1) performing cross-lingual transfer concerning different directions and 2) tackling the extremely annotation-poor scenario.
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<a href="https://link.zhihu.com/?target=https%3A//www.aclweb.org/anthology/P19-1522" >Exploring Pre-trained Language Models for Event Extraction and Geenration, ACL 2019</a> by<i> Yang, Sen and Feng, Dawei and Qiao, Linbo and Kan, Zhigang and Li, Dongsheng
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(<a>Github</a>)</summary><blockquote><p align="justify">
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动机：
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ACE事件抽取任务的传统方法通常依赖被手动注释过的数据，但是手动注释数据非常耗费精力并且也限制了数据集的规模。我们提出了一个方法来克服这个问题。
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主要思想：
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本文提出了一个基于预训练语言模型的框架，该框架包含一个作为基础的事件抽取模型以及一种生成被标记事件的方法。我们提出的事件抽取模型由触发词抽取器和论元抽取器组成，论元抽取器用前者的结果进行推理。此外，我们根据角色的重要性对损失函数重新进行加权，从而提高了论元抽取器的性能。
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数据集：ACE2005
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Keywords: Context-aware word representation, LSTM, Tensor layer
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</p></blockquote></details>
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<details/>
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<summary/>
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<a href="https://link.zhihu.com/?target=https%3A//www.aclweb.org/anthology/D19-1027/" >Open Event Extraction from Online Text using a Generative Adversarial Network, EMNLP 2019 </a> by<i> Rui Wang, Deyu Zhou, Yulan He
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(<a>Github</a>)</summary><blockquote><p align="justify">
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动机：
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提取开放域事件的结构化表示的方法通常假定文档中的所有单词都是从单个事件中生成的，因此他们通常不适用于诸如新闻文章之类的长文本。为了解决这些局限性，我们提出了一种基于生成对抗网络的事件抽取模型，称为对抗神经事件模型（AEM）。
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主要思想：
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AEM使用Dirichlet先验对事件建模，并使用生成器网络来捕获潜在事件的模式。鉴别符用于区分原始文档和从潜在事件中重建的文档。鉴别器的副产品是鉴别器网络生成的特征允许事件抽取的可视化。
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数据集：Twitter, and Google datasets
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</p></blockquote></details>
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<details/>
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<summary/>
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<a href="https://link.zhihu.com/?target=https%3A//arxiv.org/pdf/1909.02126.pdf" >Reporting the unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes, EMNLP 2019</a> by<i> Aida Mostafazadeh Davani etal.
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(<a href="https://link.zhihu.com/?target=https%3A//github.com/aiida-/HateCrime">Github</a>)</summary><blockquote><p align="justify">
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动机：
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将事件提取和多实例学习应用于本地新闻文章的语料库，可以用来预测仇恨犯罪的发生。
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主要思想：
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根据是否为仇恨罪标记每篇文章的任务被定义为多实例学习（MIL）问题。我们通过使用文章所有句子中嵌入的信息来确定文章是否报道了仇恨犯罪。在一组带注释的文章上测试了模型之后，我们将被训练过的模型应用于联邦调查局没有报道过的城市，并对这些城市中仇恨犯罪的发生频率进行了下界估计。
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</p></blockquote></details>
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### 阅读理解式事件抽取
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@ -1079,6 +1180,10 @@ The scarcity in annotated data poses a great challenge for event detection (ED).
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The scarcity in annotated data poses a great challenge for event detection (ED). Cross-lingual ED aims to tackle this challenge by transferring knowledge between different languages to boost performance. However, previous cross-lingual methods for ED demonstrated a heavy dependency on parallel resources, which might limit their applicability. In this paper, we propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources. Specifically, to construct a lexical mapping between different languages, we devise a context-dependent translation method; to treat the word order difference problem, we propose a shared syntactic order event detector for multilingual co-training. The efficiency of our method is studied through extensive experiments on two standard datasets. Empirical results indicate that our method is effective in 1) performing cross-lingual transfer concerning different directions and 2) tackling the extremely annotation-poor scenario.
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</p></blockquote></details>
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## Shallow Learning Models(浅层学习模型)
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[:arrow_up:](#table-of-contents)
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