Global Semantics with Boundary Constraint Knowledge Graph for Chinese Financial Event Detection

Abstract

Chinese financial event detection has a great significance in the application of financial risk analysis, en-terprise management and decision-making. The existing tasks of Chinese event detection are mainly regarded as character-based or word-based classification, which suffers from the ambiguity of trigger words. These tasks only concentrate on local information (e.g character and word), which loses sight of global information like sentence semantics. Furthermore, in the finance field, there exists the problem of fuzzy boundary between different event types. In this paper, we propose a global semantics with boundary constraint knowledge graph (BCKG) for Chinese financial event detection, which considers both sentence semantics and boundary knowledge. At first, Chinese financial dataset (CFD) is constructed by considering the complexity in financial area. And then, the sentence seman-tics embedding is obtained by pre-training BERT fine-tuning mechanism to address the problem of ambiguity of trigger words, which considers both syntactic information and context sentence semantics comprehensively. Finally, we construct the BCKG for financial event, which can add additional prior knowledge to solve fuzzy boundary problem. The proposed method for event detection achieves outstanding performance on standard ACE 2005 Chinese dataset and constructed CFD. The experimental results demonstrate the effectiveness of the proposed method.