论文标题
用于共同提取实体和分类关系的经常性交互网络
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
论文作者
论文摘要
使用多任务学习方法来解决实体和关系的共同提取的想法是由实体识别任务与关系分类任务之间的相关性的动机。使用多任务学习技术解决问题的现有方法通过共享网络学习两个任务之间的交互，共享信息将传递到特定于任务的网络中以进行预测。但是，这种方法阻碍了模型无法在两个任务之间学习显式相互作用，以提高各个任务的性能。作为解决方案，我们设计了一个多任务学习模型，我们称之为复发性交互网络，该网络允许动态学习相互作用，以有效地对特定于任务的特征进行分类模型。关于两个现实世界数据集的实证研究证实了所提出的模型的优越性。
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task. Existing methods using multi-task learning techniques to address the problem learn interactions among the two tasks through a shared network, where the shared information is passed into the task-specific networks for prediction. However, such an approach hinders the model from learning explicit interactions between the two tasks to improve the performance on the individual tasks. As a solution, we design a multi-task learning model which we refer to as recurrent interaction network which allows the learning of interactions dynamically, to effectively model task-specific features for classification. Empirical studies on two real-world datasets confirm the superiority of the proposed model.
