A Deep Neural Network Model for Joint Entity and Relation Extraction
A Deep Neural Network Model for Joint Entity and Relation Extraction
复制标题
用于联合实体和关系提取的深度神经网络模型
DOI：
10.1109/access.2019.2949086
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Zhang, Kai
中科院分区：
文献类型：
--
作者：
Pang, Yihe;Liu, Jie;Zhang, Kai
文本中实体及其关系的联合抽取是知识图自动构建中的一个重要问题，也称为关系三元组的联合抽取。句子中的关系三元组比较复杂，多个不同的关系三元组之间可能存在重叠，这在现实生活中是很常见的。然而，多对三胞胎不能有效地提取在大多数以前的作品。为了缓解这个问题，我们提出了一种基于序列到序列学习的深度神经网络模型，即混合双指针网络（HDP），它通过生成混合双指针序列从给定的句子中提取多对三元组。在实验中，我们使用纽约时报（NYT）公共数据集测试了我们的模型。实验结果表明，我们的模型优于国家的最先进的工作，并取得了17.1%的改善的F1值。
Joint extraction of entities and their relations from the text is an essential issue in automatic knowledge graph construction, which is also known as the joint extraction of relational triplets. The relational triplets in sentence are complicated, multiple and different relational triplets may have overlaps, which is commonly seen in reality. However, multiple pairs of triplets cannot be efficiently extracted in most of the previous works. To mitigate this problem, we propose a deep neural network model based on the sequence-to-sequence learning, namely, the hybrid dual pointer networks (HDP), which extracts multiple pairs of triplets from the given sentence by generating the hybrid dual pointer sequence. In experiments, we tested our model using the New York Times (NYT) public dataset. The experimental results demonstrated that our model outperformed the state-of-the-art work, and achieved a 17.1% improvement on the F1 values.