Chinese Relation Extraction with External Knowledge-Enhanced Semantic Understanding

Relation extraction is the foundation of constructing knowledge graphs, and Chinese relation extraction is a particularly challenging aspect of this task. Most existing methods for Chinese relation extraction rely either on character-based or word-based features. However, the former struggles to capture contextual information between characters, while the latter is constrained by the quality of word segmentation, resulting in relatively low performance. To address this issue, a Chinese relation extraction model enhanced with external knowledge for semantic understanding is proposed. This model leverages external knowledge to improve semantic understanding in the text, thereby enhancing the performance of relation prediction between entity pairs. The approach consists of three main steps: first, the ERNIE pre-trained language model is used to convert textual information into dynamic word embeddings; second, an attention mechanism is employed to enrich the semantic representation of sentences containing entities, while external knowledge is used to mitigate the ambiguity of Chinese entity words as much as possible; and finally, the semantic representation enhanced with external knowledge is used as input for classification to make predictions. Experimental results demonstrate that the proposed model outperforms existing methods in Chinese relation extraction and offers better