A two-dimensional feature-engineering method for relation extraction

Transforming a sentence into a 2D representation, similar to table structures, reveals a semantic plane where elements can represent potential relations between paired entities. The 2D representation effectively addresses the common issue of overlapping relations. However, previous works directly transformed the representation from raw input, neglecting the utilization of prior knowledge, which is essential to support the entity relation extraction task. In this paper, we propose a 2D feature engineering method. The method innovatively integrates prior knowledge within 2D sentence representations, marking an advancement over previous strategies, and employs an attention mechanism to build the hitherto overlooked association between entities and prior knowledge. The fusion of prior knowledge and targeted attention enhances the model’s relational discernment capabilities. Evaluations on three benchmark datasets—ACE05 Chinese, ACE05 English, and CLTC—demonstrate the efficacy of the proposed method. It surpasses existing techniques, achieving state-of-the-art performance with F1 score improvements of 0.88%, 3.27%, and 2.83% respectively. The results indicate that 2D feature engineering can take advantage of a 2D sentence representation and make full use of prior knowledge.