Named Entity Recognition (NER) plays a crucial role in the field of Natural Language Processing, holding significant value in applications such as information extraction, knowledge graphs, and question–answering systems. However, Chinese NER faces challenges such as semantic complexity, uncertain entity boundaries, and nested structures. To address these issues, this study proposes an innovative approach, namely Multi-Granularity BERT Adapter and Efficient Global Pointer (MGBERT-Pointer). The semantic encoding layer adopts Multi-Granularity Adapter (MGA), while the decoding layer employs Efficient Global Pointer (EGP) network, ensuring collaborative functionality. The MGA, incorporating Character Adapter, Entity Adapter, and Lexicon Adapter through interactive mechanisms, are deeply integrated into the BERT base, significantly enhancing the model’s ability to handle complex contexts and ambiguities. The EGP, utilizing Rotary Position Embedding, resolves the issue of insufficient boundary information in traditional attention mechanisms, thereby improving the model’s understanding and recognition of nested entity structures. Experimental results on four public datasets demonstrate a significant enhancement in Chinese NER performance achieved by the MGBERT-Pointer model.

Knowledge base question answering (KBQA) can be divided into two types according to the type of complexity: questions with constraints and questions with multiple hops of relationships. Previous work on knowledge base question answering have mostly focused on entities and relations. In a multihop question, it is insufficient to focus solely on topic entities and their relations since the relation between words also contains some important information. In addition, because the question contains constraints or multiple relationships, the information is difficult to capture, or the constraints are missed. In this paper, we applied a dependency structure to questions that capture relation information (e.g., constraint) between the words in question through a graph convolution network. The captured relation information is integrated into the question for re-encoding, and the information is used to generate and rank query graphs. Compared with existing sequence models and query graph generation models, our approach achieves a 0.8–3% improvement on two benchmark datasets.