Efficient Chinese Relation Extraction with Multi-entity Dependency Tree Pruning and Path-Fusion

Relation Extraction (RE) is a crucial task in natural language processing that aims to predict the relationship between two given entities. In recent years, a large majority of approaches utilized syntactic information, particularly dependency trees, to enhance relation extraction by providing superior semantic guidance. Compared with other fields, Chinese texts are more semantic complex, and contain multiple pairs of entities. However, many studies only focus on removing extraneous information from the dependency tree that pertains to a single entity pair. We hypothesis that preserving the semantic and structural interaction between multiple entity pairs in the tree is more conducive to the identification of the current entity pair relationship. Therefore, we propose a new pruning strategy called Multi-entity dependency Tree Pruning and path-Fusion (MTPF), which preserves the ancestor nodes of each entity pair to their lowest common ancestor, as well as the shortest path from that node to each entity. Then we introduce A-GCN as the encoder for the syntax tree obtained above, and the idea of multi-classification sequence as the decoder. Experimental results on two Chinese benchmark datasets, the financial dataset constructed by ourselves and DUIE1.0, demonstrate the effectiveness of our pruning strategy for CRE, where our approach outperforms strong dependency-tree baselines and achieve state-of-the-art results on both datasets.