BERT-PointerNet: A Unified Framework for Cross-Sentence Entity-Relation Extraction in Chinese Computer Science Texts

This study presents an innovative text annotation framework for constructing knowledge graphs in the Chinese computer science domain, addressing challenges such as nested entity resolution and implicit relation extraction in Chinese technical texts. The proposed method integrates relation extraction into named entity recognition NER via a novel CS_R_BMES tagging schema, which extends the BMES Begin-Middle-End-Single approach to encode both entity boundaries and relation types. By appending a fully connected layer to the BERT model, we generate domain-specific word embeddings that align with the CS_R_BMES annotation space. These embeddings are then fed into a BERT-BiLSTM-CRF-PointerNet architecture, where a Pointer Network decodes CRF-generated labels into structured triples, dynamically resolving nested entities and implicit relations through cross-attention mechanisms. Experimental results demonstrate a 4.19 F1 score improvement over baseline models, with the proposed model achieving 93.7 F1 for entity-relation extraction. Ablation studies confirm the critical role of BERT�s contextual encoding and the Pointer Network�s capability to handle complex linguistic phenomena. Notably, this framework exhibits strong generalizability, enabling cross-domain adaptation to fields like software engineering by adjusting entity'elation categories. The constructed knowledge graph provides a scalable foundation for educational applications in computer science.