A Novel Tensor Learning Model for Joint Relational Triplet Extraction
提出一种基于Tucker分解的张量学习模型，利用三维词关系张量捕捉语义关系间的相关性，在NYT和WebNLG数据集上F1分数比现有最优方法高3.2%。
The relational triplet is a format to represent relational facts in the real world, which consists of two entities and a semantic relation between these two entities. Since the relational triplet is the essential component in a knowledge graph (KG), extracting relational triplets from unstructured texts is vital for KG construction and has attached increasing research interest in recent years. In this work, we find that relation correlation is common in real life and could be beneficial for the relational triplet extraction task. However, existing relational triplet extraction works neglect to explore the relation correlation that bottlenecks the model performance. Therefore, to better explore and take advantage of the correlation among semantic relations, we innovatively utilize a three-dimension word relation tensor to describe relations between words in a sentence. Then, we treat the relation extraction task as a tensor learning problem and propose an end-to-end tensor learning model based on Tucker decomposition. Compared with directly capturing correlation among relations in a sentence, learning the correlation of elements in a three-dimension word relation tensor is more feasible and could be addressed through tensor learning methods. To verify the effectiveness of the proposed model, extensive experiments are also conducted on two widely used benchmark datasets, that is, NYT and WebNLG. Results show that our model outperforms the state-of-the-art by a large margin of F1 scores, such as the developed model has an improvement of 3.2% on the NYT dataset compared to the state-of-the-art. Source codes and data can be found at https://github.com/Sirius11311/TLRel.git.