Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, ZongYu Wang, Rui Xie, Wei Wu, Man Lan. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Entity alignment (EA) aims to find entities in different knowledge graphs (KGs) that refer to the same object in the real world. Recent studies incorporate temporal information to augment the representations of KGs. The existing methods for EA between temporal KGs (TKGs) utilize a time-aware attention mechanisms to incorporate relational and temporal information into entity embeddings. The approaches outperform the previous methods by using temporal information. However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations. Therefore, we propose a simple GNN model combined with a temporal information matching mechanism, which achieves better performance with less time and fewer parameters. Furthermore, since alignment seeds are difficult to label in real-world applications, we also propose a method to generate unsupervised alignment seeds via the temporal information of TKG. Extensive experiments on public datasets indicate that our supervised method significantly outperforms the previous methods and the unsupervised one has competitive performance.

Event extraction aims to detect events and extract event arguments. However, various events are not only too nuanced and complex to distinguish, but also involve multiple entities in the real-world scenario, especially in the financial field. This brings a great challenge to the current event extraction. To address these problems, previous event-centric methods detect events first and then extract arguments. Due to the diversity and complexity of events, event detection has a low performance, which is unfit for the huge amount of news in the real world. Given that the performance of named entity recognition (NER) is satisfactory, we shift our perspective from event-centric to target-centric view. In this paper, we propose a new task: target-dependent event detection (TDED), which aims to extract target entities and detect their corresponding events. We also propose a semantic and syntactic aware approach to support thousands of target entity extraction first and dozens of event types detection, that can be applied to massive corpora. Experimental results on a real-world Chinese financial dataset demonstrate that our model outperforms previous methods, especially in complex scenarios.