Abstract
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.
Abstract (translated)
关系提取(RE)的任务是从文本中提取实体之间的关系。大多数关系提取方法从自由形式的连续文本中提取关系,而忽略其他丰富的数据源,如表格。我们从表格组织数据的角度探讨RE。我们介绍了一个由卷积神经网络(CNN)和双向长短时记忆网络(BiLSTM)组成的新模型,分别编码实体并学习它们之间的依赖关系。我们在一个大型最近的数据集上评估了我们的模型,并与以前的神经网络方法进行比较。实验结果表明,我们的模型在表格数据的关系提取任务中 consistently outperforms the previous model。我们进行了全面的错误分析和微分研究,以显示模型的各种组件的贡献。最后,我们讨论了我们方法的有用性和权衡,并提供了促进进一步研究的建议。
URL
https://arxiv.org/abs/2307.05827
