论文标题
基于技术专利的特征推理的关系三重提取方法
A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents
论文作者
论文摘要
基于表填充的关系三元提取方法可以解决关系重叠和偏见传播的问题。但是，其中大多数仅为每个关系建立单独的表特征，这忽略了不同实体对与不同关系特征之间的隐式关系。因此，提出了一种基于表填充技术专利的表填充的特征推理三重提取方法，以探索实体识别和实体关系的整合，并从多源科学和技术专利数据中提取实体关系三元。与以前的方法相比，我们为关系三重提取提出的方法具有以下优点：1）表填充方法可节省更多的运行空间，从而增强了模型的速度和效率。 2）基于现有令牌对和表关系的特征，推理隐式关系特征，并提高三重提取的准确性。在五个基准数据集上，我们评估了我们建议的模型。结果表明我们的模型是高级且有效的，并且在大多数这些数据集上都表现良好。
The relation triples extraction method based on table filling can address the issues of relation overlap and bias propagation. However, most of them only establish separate table features for each relationship, which ignores the implicit relationship between different entity pairs and different relationship features. Therefore, a feature reasoning relational triple extraction method based on table filling for technological patents is proposed to explore the integration of entity recognition and entity relationship, and to extract entity relationship triples from multi-source scientific and technological patents data. Compared with the previous methods, the method we proposed for relational triple extraction has the following advantages: 1) The table filling method that saves more running space enhances the speed and efficiency of the model. 2) Based on the features of existing token pairs and table relations, reasoning the implicit relationship features, and improve the accuracy of triple extraction. On five benchmark datasets, we evaluated the model we suggested. The result suggest that our model is advanced and effective, and it performed well on most of these datasets.
