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4.2. 论文解读
自动识别句子中实体之间具有的某种语义关系。根据参与实体的多少可以分为二元关系抽取（两个实体）和多元关系抽取（三个及以上实体）。
通过关注两个实体间的语义关系，可以得到（arg1, relation, arg2）三元组，其中arg1和arg2表示两个实体，relation表示实体间的语义关系。
根据处理数据源的不同，关系抽取可以分为以下三种：
根据抽取文本的范围不同，关系抽取可以分为以下两种：
根据所抽取领域的划分，关系抽取又可以分为以下两种：
限定域关系抽取方法：
ACE 2005
数据集简介：ACE2005语料库是语言数据联盟(LDC)发布的由实体，关系和事件注释组成的各种类型的数据，包括英语，阿拉伯语和中文培训数据，目标是开发自动内容提取技术，支持以文本形式自动处理人类语言。ACE语料解决了五个子任务的识别：entities、values、temporal expressions、relations and events。这些任务要求系统处理文档中的语言数据，然后为每个文档输出有关其中提到或讨论的实体，值，时间表达式，关系和事件的信息。
获取方式：数据集收费，需在LDC联盟的官网上注册再购买，LDC账号注册地址 ACE 2005 下载地址
TACRED
数据集简介：TACRED(TAC Relation Extraction Dataset)是一个拥有106264条实例的大规模关系抽取数据集，这些数据来自于每年的TAC KBP（TAC Knowledge Base Population）比赛使用的语料库中的新闻专线和网络文本。包含了41关系类型，此外若句子无定义关系，被标注成no_relation类型。数据集的详细介绍可以访问TACRED文档
获取方式：数据集收费，需在LDC联盟官网注册会员再购买 LDC账号注册地址 TACRED 下载地址
SemEval2010_task8
数据集简介:对于给定了的句子和两个做了标注的名词，从给定的关系清单中选出最合适的关系。数据集一共9种关系类别数，此外包含一类Other关系，含有6674实例数量。
获取方式: 原始数据
FewRel
数据集简介:FewRel是目前最大规模的精标注关系抽取数据集，由孙茂松教授领导的清华大学自然语言处理实验室发布。一共100种关系类别数，含有70000实例数量。
获取方式：FewRel 网站地址 论文地址
NYT10
NYT-10数据集文本来源于纽约时报，命名实体是通过 Stanford NER 工具并结合 Freebase 知识库进行标注的。实体对之间的关系是链接Freebase知识库中的关系，结合远监督方法所得到。该数据集共含有53种关系类型，包括特殊关系类型NA，即头尾实体无关系。
获取方式:原始数据
获取更多关系抽取数据集，可访问此处Annotated-Semantic-Relationships-Datasets
二分类：
Accuracy = (预测正确的样本数)/(总样本数)=(TP+TN)/(TP+TN+FP+FN)
Precision = (预测为正例且正确预测的样本数)/(所有预测为正例的样本数) = TP/(TP+FP)
Recall = (预测为正例且正确预测的样本数)/(所有真实情况为正例的样本数) = TP/(TP+FN)
F1 = 2 * (Precision * Recall) / (Precision + Recall )
多分类：
Macro Average
多类别（N类） F1/P/R的计算，即计算N个类别的F1/P/R，每次计算以当前类别为正例，其他所有类别为负例，最终将各类别结果求和并除以类别数取平均。
Micro Average
统计当前类别的TP、TN、FP、FN数量，再将该四类样本数各自求和作为新的TP、TN、FP、FN，计算F1/P/R公式同二分类。
P@N（最高置信度预测精度）:
通常在远监督关系抽取中使用到，由于知识库所含关系实例的不完善，会出现高置信度包含关系实例的实体对被叛为负例，从而低估了系统正确率。此时可以采用人工评价，将预测结果中知识库已包含的三元组移除，然后人工判断抽取关系实例是否正确，按照top N的准确率对抽取效果进行评价。
Relation Extraction on TACRED：
| 模型 | average F1 | 论文题目 | 年份 | 论文链接 | code |
|---|---|---|---|---|---|
| BERTEM+MTB | 71.5 | Matching the Blanks: Distributional Similarity for Relation Learning | 2019 | https://arxiv.org/pdf/1906.03158v1.pdf | https://github.com/plkmo/BERT-Relation-Extraction |
| KnowBert-W+W | 71.5 | Knowledge Enhanced Contextual Word Representations | 2019 | https://arxiv.org/pdf/1909.04164v2.pdf | |
| DG-SpanBERT | 71.5 | Efficient long-distance relation extraction with DG-SpanBERT | 2020 | https://arxiv.org/pdf/2004.03636v1.pdf | |
| SpanBERT | 70.8 | SpanBERT: Improving Pre-training by Representing and Predicting Spans | 2019 | https://arxiv.org/pdf/1907.10529v3.pdf | https://github.com/facebookresearch/SpanBERT |
| R-BERT | 69.4 | Enriching Pre-trained Language Model with Entity Information for Relation Classification | 2020 | https://arxiv.org/pdf/1905.08284v1.pdf | https://github.com/wang-h/bert-relation-classification |
| C-GCN + PA-LSTM | 68.2 | Graph Convolution over Pruned Dependency Trees Improves Relation Extraction | 2018 | https://arxiv.org/pdf/1809.10185v1.pdf | https://github.com/qipeng/gcn-over-pruned-trees |
Relation Extraction on SemEval-2010 Task 8：
| 模型 | average F1 | 论文题目 | 年份 | 论文链接 | code |
|---|---|---|---|---|---|
| Skeleton-Aware BERT | 90.36 | Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts | 2019 | https://arxiv.org/pdf/1912.01858v1.pdf | https://github.com/wang-h/bert-relation-classification |
| EPGNN | 90.2 | Improving Relation Classification by Entity Pair Graph | 2019 | http://proceedings.mlr.press/v101/zhao19a/zhao19a.pdf | |
| BERTEM+MTB | 89.5 | Matching the Blanks: Distributional Similarity for Relation Learning | 2019 | https://arxiv.org/pdf/1906.03158v1.pdf | https://github.com/plkmo/BERT-Relation-Extraction |
| R-BERT | 89.25 | Enriching Pre-trained Language Model with Entity Information for Relation Classification | 2020 | https://arxiv.org/pdf/1905.08284v1.pdf | https://github.com/wang-h/bert-relation-classification |
| KnowBert-W+W | 89.1 | Knowledge Enhanced Contextual Word Representations | 2019 | https://arxiv.org/pdf/1909.04164v2.pdf | |
| Entity-Aware BERT | 89 | Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers | 2019 | https://arxiv.org/pdf/1902.01030v2.pdf | https://github.com/helloeve/mre-in-one-pass |
Relation Extraction on ACE 2005：
| 模型 | RELATION F1 | ENTITY F1 | SENTENCE ENCODER | 论文题目 | 年份 | 论文链接 | code |
|---|---|---|---|---|---|---|---|
| MRC4ERE++ | 62.1 | 85.5 | BERT base | Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction | 2020 | https://www.ijcai.org/Proceedings/2020/0546.pdf | https://github.com/TanyaZhao/MRC4ERE |
| Multi-turn QA | 60.2 | 84.8 | BERT base | Entity-Relation Extraction as Multi-Turn Question Answering | 2019 | https://arxiv.org/pdf/1905.05529v4.pdf | |
| MRT | 59.6 | 83.6 | biLSTM | Extracting Entities and Relations with Joint Minimum Risk Training | 2018 | https://www.aclweb.org/anthology/D18-1249 | |
| GCN | 59.1 | 84.2 | biLSTM | Joint Type Inference on Entities and Relations via Graph Convolutional Networks | 2019 | https://www.aclweb.org/anthology/P19-1131 | |
| Global | 57.5 | 83.6 | biLSTM | End-to-End Neural Relation Extraction with Global Optimization | 2017 | https://www.aclweb.org/anthology/D17-1182 | |
| SPTree | 55.6 | 83.4 | biLSTM | End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures | 2016 | https://arxiv.org/pdf/1601.00770v3.pdf | https://github.com/tticoin/LSTM-ER |
Relation Extraction on ACE 2004：
| 模型 | RELATION F1 | ENTITY F1 | 论文题目 | 年份 | 论文链接 | code |
|---|---|---|---|---|---|---|
| DYGIE | 59.7 | 87.4 | A General Framework for Information Extraction using Dynamic Span Graphs | 2019 | https://arxiv.org/pdf/1904.03296v1.pdf | https://github.com/luanyi/DyGIE |
| Multi-turn QA | 49.4 | 83.6 | Entity-Relation Extraction as Multi-Turn Question Answering | 2019 | https://arxiv.org/pdf/1905.05529v4.pdf | |
| SPTree | 48.4 | 81.8 | End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures | 2016 | https://arxiv.org/pdf/1601.00770v3.pdf | https://github.com/tticoin/LSTM-ER |
| multi-head + AT | 47.45 | 81.64 | Adversarial training for multi-context joint entity and relation extraction | 2018 | https://arxiv.org/pdf/1808.06876v3.pdf | https://github.com/bekou/multihead_joint_entity_relation_extraction |
| multi-head | 47.14 | 81.16 | Joint entity recognition and relation extraction as a multi-head selection problem | 2018 | https://arxiv.org/pdf/1804.07847v3.pdf | https://github.com/bekou/multihead_joint_entity_relation_extraction |
| Attention | 45.7 | 79.6 | Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees | 2017 | https://www.aclweb.org/anthology/P17-1085 |
Relation Extraction on NYT：
| 模型 | average F1 | 论文题目 | 年份 | 论文链接 | code |
|---|---|---|---|---|---|
| REDN | 89.8 | Downstream Model Design of Pre-trained Language Model for Relation Extraction Task | 2020 | https://arxiv.org/pdf/2004.03786v1.pdf | https://github.com/slczgwh/REDN |
| CASREL | 89.6 | A Novel Cascade Binary Tagging Framework | 2019 | https://arxiv.org/pdf/1909.03227v4.pdf | https://github.com/weizhepei/CasRel |
| HBT | 89.5 | A Novel Cascade Binary Tagging Framework for Relational Triple Extraction | 2019 | https://arxiv.org/pdf/1909.03227v4.pdf | https://github.com/weizhepei/CasRel |
| WDec | 84.4 | Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction | 2019 | https://arxiv.org/pdf/1911.09886v1.pdf | https://github.com/nusnlp/PtrNetDecoding4JERE |
| ETL-Span | 78.0 | Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy | 2019 | https://arxiv.org/pdf/1909.04273v3.pdf | https://github.com/yubowen-ph/JointER |
| CopyRE' OneDecoder | 72.2 | CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning | 2019 | https://arxiv.org/pdf/1911.10438v1.pdf | https://github.com/WindChimeRan/CopyMTL |
Relation Extraction on CoNLL04：
| 模型 | RELATION F1 | ENTITY F1 | 论文题目 | 年份 | 论文链接 | code |
|---|---|---|---|---|---|---|
| SpERT | 71.47 | 88.94 | Span-based Joint Entity and Relation Extraction with Transformer Pre-training | 2019 | https://arxiv.org/pdf/1909.07755v3.pdf | https://github.com/markus-eberts/spert |
| Multi-turn QA | 68.9 | 87.8 | Entity-Relation Extraction as Multi-Turn Question Answering | 2019 | https://arxiv.org/pdf/1905.05529v4.pdf | |
| Global | 67.8 | 85.6 | End-to-End Neural Relation Extraction with Global Optimization | 2017 | https://www.aclweb.org/anthology/D17-1182 | |
| Biaffine attention | 64.40 | 86.20 | End-to-end neural relation extraction using deep biaffine attention | 2018 | https://arxiv.org/pdf/1812.11275v1.pdf | https://github.com/datquocnguyen/jointRE |
| Relation-Metric with AT | 62.29 | 84.15 | Neural Metric Learning for Fast End-to-End Relation Extraction | 2019 | https://arxiv.org/pdf/1905.07458v4.pdf | |
| multi-head | 62.04 | 83.9 | Joint entity recognition and relation extraction as a multi-head selection problem | 2018 | https://arxiv.org/pdf/1804.07847v3.pdf | https://github.com/bekou/multihead_joint_entity_relation_extraction |
Relation Extraction on FewRel：
| 模型 | average F1 | 论文题目 | 年份 | 论文链接 | code |
|---|---|---|---|---|---|
| ERNIE | 88.32 | ERNIE: Enhanced Language Representation with Informative Entities | 2019 | https://arxiv.org/pdf/1905.07129v3.pdf | https://github.com/thunlp/ERNIE |
| 论文题目 | 抽取任务 | 关键词 | 论文链接 | 会议及年份 | code |
|---|---|---|---|---|---|
| Attention Guided Graph Convolutional Networks for Relation Extraction | 关系提取 | 注意力导向图卷积网络（AGGCN）；语义依赖树；软修剪；自动学习子结构； | https://www.aclweb.org/anthology/P19-1024.pdf | ACL2019 | |
| A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction | 关系提取 | 最短依赖路径SDP；注意力模型；深度神经模型；LSTM网络；CNN | https://www.aclweb.org/anthology/N19-1298 | NAACL 2019 | https://github.com/catcd/RbSP |
| 论文题目 | 抽取任务 | 关键词 | 论文链接 | 会议及年份 | code |
|---|---|---|---|---|---|
| Joint Type Inference on Entities and Relations via Graph Convolutional Networks | 抽取三元组的joint任务 | 实体关系联合推断；图卷积模型（GCN）；二元关系分类 | https://pdfs.semanticscholar.org/7ce8/ce2768907421fb1a6cbfe13a8a36992721a7.pdf | ACL2019 | |
| GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction | 抽取三元组的joint任务 | 端到端关系抽取；图卷积网络； | https://tsujuifu.github.io/pubs/acl19_graph-rel.pdf | ACL2019 | |
| Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data | 关系抽取 | BIO字符/词嵌入；多任务体系结构；关系分类 | https://arxiv.org/pdf/1906.08931.pdf | ACL2019 | |
| Entity-Relation Extraction as Multi-turn Question Answering | 关系抽取 | 多回合QA；上下文识别答案范围任务 | https://arxiv.org/pdf/1905.05529.pdf | ACL2019 | |
| Graph Neural Networks with Generated Parameters for Relation | 关系抽取 | 图神经网络（GNN）；多跳关系推理 | https://arxiv.org/pdf/1902.00756.pdf | ACL2019 | |
| Kernelized Hashcode Representations for Biomedical Relation Extraction | 关系分类 | 核化的局部敏感哈希（KLSH）；降低计算成本 | https://arxiv.org/pdf/1711.04044.pdf | ACL2019 | |
| Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs | 关系抽取 | 图神经网络模型；文档级关系提取 | https://arxiv.org/pdf/1909.00228v1.pdf | EMNLP2019 |
| 论文题目 | 抽取任务 | 关键词 | 论文链接 | 会议及年份 | code |
|---|---|---|---|---|---|
| Hybrid Attention-based Prototypical Networks for Noisy Few-Shot Relation Classification | 关系分类 | 远监督；噪声；混合注意力圆形网络 | https://gaotianyu1350.github.io/assets/aaai2019_hatt_paper.pdf | AAAI2019 | https://github.com/thunlp/HATT-Proto |
| A Hierarchical Framework for Relation Extraction with Reinforcement Learning | 关系提取 | 增强关系类型系和实体交互；分层强化学习（HRL）框架；远监督数据集 | https://arxiv.org/pdf/1811.03925.pdf | AAAI2019 | |
| Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction | 关系提取 | 远监督抗噪；Cross-relation Cross-bag Selective Attention；多实例学习；句子级别；注意力机制；关注高质量实体对 | https://arxiv.org/pdf/1812.10604.pdf | AAAI2019 | |
| Structured Minimally Supervised Learning for Neural Relation Extraction | 关系提取 | 最小监督；学习的表示形式；结构化学习 | https://arxiv.org/pdf/1904.00118.pdf | NAACL2019 | |
| Combining Distant and Direct Supervision for Neural Relation Extraction | 关系提取 | 降噪；监督学习+远监督模型 | https://arxiv.org/pdf/1810.12956.pdf | NAACL2019 | https://github.com/allenai/comb_dist_direct_relex/ |
| Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions | 关系提取 | 句子级别的Attention； | https://www.aclweb.org/anthology/N19-1288.pdf | NAACL2019 | |
| GAN Driven Semi-distant Supervision for Relation Extraction | 关系提取 | 半远监督；生成对抗网络（GAN） | https://www.aclweb.org/anthology/N19-1307 | NAACL 2019 | |
| Improving Distantly-Supervised Relation Extraction with Joint Label Embedding | 关系提取 | 多层注意力模型；联合标签嵌入 | https://www.aclweb.org/anthology/D19-1395.pdf | NAACL 2019 | |
| Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction | 关系提取 | 协作式学习；卷积神经网（CNN)；卷积运算内部自注意机制 | https://www.aclweb.org/anthology/D19-1037.pdf | NAACL 2019 |
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