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https://github.com/loujie0822/DeepIE
DeepIE: Deep Learning for Information Extraction
https://github.com/loujie0822/DeepIE
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DeepIE: Deep Learning for Information Extraction
- Host: GitHub
- URL: https://github.com/loujie0822/DeepIE
- Owner: loujie0822
- Created: 2020-01-07T09:51:24.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-12-09T00:09:12.000Z (over 1 year ago)
- Last Synced: 2023-10-20T21:19:25.515Z (8 months ago)
- Language: Python
- Homepage: https://github.com/loujie0822/DeepIE
- Size: 1.48 MB
- Stars: 1,879
- Watchers: 49
- Forks: 361
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
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- awesome-stars-copy - loujie0822/DeepIE - DeepIE: Deep Learning for Information Extraction (Python)
README
# DeepIE: Deep Learning for Information Extraction
**DeepIE**： 基于深度学习的信息抽取技术（预计2020年8月31日前全部更新完毕）
## TOP
- **知乎专栏文章**：[nlp中的实体关系抽取方法总结](https://github.com/loujie0822/DeepIE/blob/jielou/docs/实体关系抽取算法总结.md)
- **知乎专栏文章**：[如何有效提升中文NER性能？词汇增强方法总结](https://zhuanlan.zhihu.com/p/142615620)
- **知乎专栏文章**：[如何解决Transformer在NER任务中效果不佳的问题？](https://zhuanlan.zhihu.com/p/137315695)
## Papers
- [ACL2020信息抽取相关论文汇总](https://github.com/loujie0822/DeepIE/blob/master/docs/ACL2020信息抽取相关论文汇总.md)
- [IJCAI2020信息抽取相关论文汇总](https://github.com/loujie0822/DeepIE/blob/master/docs/IJCAI2020_%E4%BF%A1%E6%81%AF%E6%8A%BD%E5%8F%96%E7%9B%B8%E5%85%B3%E8%AE%BA%E6%96%87%E5%90%88%E9%9B%86%20.md)
- [2019各顶会中的关系抽取论文汇总](https://github.com/loujie0822/DeepIE/blob/master/docs/2019各顶会中的关系抽取论文]汇总.md)
- [事件抽取论文汇总](https://github.com/loujie0822/DeepIE/blob/master/docs/事件抽取论文汇总.md)
- [历年来NER论文汇总](https://github.com/loujie0822/DeepIE/blob/master/docs/历年来NER论文汇总.md)
## Codes
#### 1. 实体抽取
- **各主流方法在主要中文NER数据集上的表现情况** [具体说明](https://github.com/loujie0822/DeepIE/blob/master/docs/各主流方法在中文NER上的表现情况.md)
| | **lexicon** | **Ontonotes** | **MSRA** | **Resume** | **Weibo** |
| -------------- | ----------- | ------------- | --------- | ---------- | --------- |
| biLSTM | ---- | 71.81 | 91.87 | 94.41 | 56.75 |
| Lattice LSTM | 词表1 | 73.88 | 93.18 | 94.46 | 58.79 |
| WC-LSTM | 词表1 | 74.43 | 93.36 | 94.96 | 49.86 |
| LR-CNN | 词表1 | 74.45 | 93.71 | 95.11 | 59.92 |
| CGN | 词表2 | 74.79 | 93.47 | 94.12 | 63.09 |
| LGN | 词表1 | 74.85 | 93.63 | 95.41 | 60.15 |
| Simple-Lexicon | 词表1 | 75.54 | 93.50 | **95.59** | 61.24 |
| FLAT | 词表1 | **76.45** | 94.12 | 95.45 | 60.32 |
| FLAT | 词表2 | 75.70 | **94.35** | 94.93 | **63.42** |
| BERT | ---- | 80.14 | 94.95 | 95.53 | 68.20 |
| BERT+FLAT | 词表1 | **81.82** | **96.09** | **95.86** | **68.55** |
- **MSRA-NER**
| 方法 | f | p | r |
| -------------------------------------------- | ---------- | ---------- | ---------- |
| char+ lstm-crf | 86.18% | 88.43% | 83.10% |
| char-bigram + lstm-crf | 91.80% | 92.60% | 90.34% |
| char-bigram + adTransformer-crf | 92.98% | 93.25% | 92.72% |
| char-bigram + lexion-augment + lstm-crf | 93.33% | 94.26% | 92.43% |
| char-bigram-BERT + lstm-crf | 94.71% | 95.14% | 94.27% |
| char-bigram-BERT + lexion-augment + lstm-crf | **95.26%** | **95.90%** | **94.63%** |
- **CCKS2019-医疗实体抽取**
| 方法 | f | p | r |
| -------------------------------------------- | ---------- | ---------- | ---------- |
| char-bigram + lstm-crf | 81.76% | 82.91% | 80.6 |
| + domain transfer（from ccks2018 to 2019） | 82.54% | 83.43% | 81.81% |
| char-bigram + adTransformer-crf | 82.83% | 82.19% | 83.49% |
| char-bigram + lexion-augment + lstm-crf | 82.76% | 82.79% | 82.72% |
| BERT-finetune+crf | 83.49% | 84.11% | 82.89% |
| roBERTa-finetune+crf | 83.66% | 83.67% | 83.66% |
| char-bigram-BERT + lstm-crf | 83.37% | 83.51% | 83.22% |
| char-bigram-BERT + lexion-augment + lstm-crf | **84.15%** | **84.29%** | **84.01%** |
- **CCKS2020-医疗实体抽取**：
(注：测试集与ccks2019一致，去除ccks2020训练集中已经在2019测试集中的样本，下列指标未做规则处理和模型融合)
| 方法 | f | p | r |
| -------------------------------------------- | ------ | ------ | ------ |
| char-bigram + lstm-crf | 82.68% | 83.14% | 82.22% |
| char-bigram + lexion-augment + lstm-crf | 83.12% | 83.10% | 83.14% |
| char-bigram-BERT + lstm-crf | 83.12% | 83.04% | 83.21% |
| char-bigram-BERT-RoBerta_wwm + lstm-crf | 83.66% | 83.76% | 83.56% |
| char-bigram-BERT-XLNet + lstm-crf | 84.12% | 83.88% | 84.36% |
| char-bigram-BERT + lexion-augment + lstm-crf | 84.50% | 84.32% | 84.67% |
- **CCKS2020-面向试验鉴定的命名实体识别任务**：TODO
#### 2. 实体关系联合抽取
[具体使用说明](https://github.com/loujie0822/DeepIE/blob/master/docs/关系抽取run说明.md)
- 2019语言与智能技术竞赛：关系抽取任务
| 方法 | f(dev) | p(dev) | r(dev) |
| ------------------------------------------ | ---------- | ---------- | ---------- |
| multi head selection | 76.36 | 79.24 | 73.69 |
| ETL-BIES | 77.07% | 77.13% | 77.06% |
| ETL-Span | 78.94% | 80.11% | 77.8% |
| ETL-Span + word2vec | 79.99% | 80.62% | 79.38% |
| ETL-Span + word2vec + adversarial training | 80.38% | 79.95% | 80.82% |
| ETL-Span + BERT | **81.88%** | **82.35%** | **81.42%** |
- 2020语言与智能技术竞赛：关系抽取任务
| 方法 | f(dev) | p(dev) | r(dev) |
| --------------- | ------ | ------ | ------ |
| ETL-Span + BERT | 74.58 | 74.44 | 74.71 |
#### 3. 属性抽取
- **领域数据集：瑞金医院糖尿病信息抽取数据**
```
# 药物-属性
['药品-用药频率','药品-持续时间','药品-用药剂量','药品-用药方法','药品-不良反应']
# 疾病-属性
['疾病-检查方法','疾病-临床表现','疾病-非药治疗','疾病-药品名称','疾病-部位']
```
| 主体 | 方法 | f | p | r |
| ---- | ---------------------------------- | ----- | ----- | ----- |
| 疾病 | lstm+ multi-label pointer network | 76.55 | 74.36 | 78.86 |
| 疾病 | bert + multi-label pointer network | 77.59 | 77.45 | 77.74 |
| 药物 | lstm+ multi-label pointer network | 81.12 | 79.15 | 83.19 |
#### 4. 实体链接/标准化
#### 5.事件抽取
- **CCKS2020-医疗事件抽取**
- **CCKS2020：面向金融领域的篇章级事件主体抽取**
- **CCKS2020：面向金融领域的篇章级事件要素抽取**
#### 6.信息抽取中的低资源解决方案
## TODO-list
- [ ] 信息抽取领域的数据资源汇总：
- 医疗
- 金融
- 电商
- 法律
- [ ] 信息抽取相关竞赛汇总：
- 百度-2020语言与智能技术竞赛：关系抽取任务
- 百度-2020语言与智能技术竞赛：事件抽取任务
- 百度-2019语言与智能技术竞赛：信息抽取
- CCKS 2019 医疗命名实体识别
- CHIP 2019 临床术语标准化任务
- CCKS 2019 人物关系抽取
- CCKS 2019 公众公司公告信息抽取
- CCKS 2019 面向金融领域的事件主体抽取
- 摘要抽取
- 前沿技术在信息抽取中的应用
## Reference