NLP/Event-Extraction
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# 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
##事件抽取的定义
## 事件抽取的定义
（1） Closed-domain
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[:arrow_up:](#table-of-contents)
###事件抽取
### 事件抽取
#### 2020
<details/>
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###事件检测
### 事件检测
#### 2019
<details/>
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</p></blockquote></details>
###中文事件抽取
### 中文事件抽取
#### 2019
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###半监督\远程监督事件抽取
### 半监督\远程监督事件抽取
#### 2019
<details/>
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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.
</p></blockquote></details>
###开放域事件抽取
### 开放域事件抽取
#### 2019
<details/>
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</p></blockquote></details>
###多语言事件抽取
### 多语言事件抽取
#### 2019
<details/>
@ -228,7 +228,7 @@ The scarcity in annotated data poses a great challenge for event detection (ED).
###数据生成
### 数据生成
#### 2019
<details/>
@ -237,7 +237,7 @@ The scarcity in annotated data poses a great challenge for event detection (ED).
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.
</p></blockquote></details>
###阅读理解式事件抽取
### 阅读理解式事件抽取
#### 2019