Relation extraction (RE) is the task of extracting relations between entities in text. Most RE methods extract relations from free-form running text and leave out other rich data sources, such as tables. We explore RE from the perspective of applying neural methods on tabularly organized data. We introduce a new model consisting of Convolutional Neural Network (CNN) and Bidirectional-Long Short Term Memory (BiLSTM) network to encode entities and learn dependencies among them, respectively. We evaluate our model on a large and recent dataset and compare results with previous neural methods. Experimental results show that our model consistently outperforms the previous model for the task of relation extraction on tabular data. We perform comprehensive error analyses and ablation study to show the contribution of various components of our model. Finally, we discuss the usefulness and trade-offs of our approach, and provide suggestions for fostering further research.

翻译：关系抽取（Relation Extraction，RE）是指从文本中抽取实体间关系的任务。大多数RE方法从自由形式的连续文本中抽取关系，忽略了其他丰富的数据源，例如表格。我们探讨从神经方法应用于表格化组织数据的角度进行关系抽取。我们引入了一种新模型，该模型由卷积神经网络（CNN）和双向长短期记忆网络（BiLSTM）组成，分别用于编码实体和学习实体间的依赖关系。我们在一个大型且较新的数据集上评估我们的模型，并将结果与先前的神经方法进行比较。实验结果表明，我们的模型在表格数据上的关系抽取任务中始终优于先前模型。我们进行了全面的错误分析和消融研究，以展示模型各组成部分的贡献。最后，我们讨论了该方法的实用性及权衡，并为促进进一步研究提供了建议。