In recent years, joint entity–relation extraction (ERE) models have become a hot research topic in natural language processing (NLP). Several studies have proposed a span-based ERE framework, which utilizes simple span embeddings for entity and relation classification. This framework addresses the issues of overlap and error propagation that were present in previous entity–relation extraction models. However, span-based models overlook the influence of lexical information on the semantic representation of the span and fail to consider relations with a strong intrinsic connection between span pairs. To tackle these aforementioned issues, we present a new ERE model called ER-LAC (Span-based Joint Entity and Relation Extraction Model with Multi-level Lexical and Attention on Context Features). This model is designed with multi-granularity lexical features to enhance the semantic representation of spans, and a transformer classifier is employed to capture the internal connections between span pairs, thereby improving the performance of relational classification. To demonstrate the effectiveness of the proposed model, ablation experiments were conducted on the CoNLL04 dataset. The proposed model was also compared with other models on three datasets, showcasing its computational efficiency. The results indicate that the introduced lexical features and classifier enhance the F1 score for entity extraction by 0.84% to 2.04% and improve the F1 score for relationship classification by 0.96% to 2.26% when compared to the previous state-of-the-art (SOTA) model and the baseline SpERT model, respectively.

Model fusion is a technique of growing interest in the field of machine learning, which constructs a generalized model by merging the parameters of multiple independent models with different capabilities without the need to access the original training data or perform costly computations. However, during model fusion, when the number of parameters in a large language model is high, the dimension of the parameter space increases, which makes it more challenging to find the optimal combination of weights. Meanwhile, there is considerable potential for further development in sustainable optimization schemes for task-specific performance enhancement through model fusion in this area. In this paper, we propose a large-scale language model fusion approach based on task-enhanced reinforcement learning (RL–Fusion) to efficiently explore and optimize model fusion configurations. The key innovation of RL–Fusion lies in its use of reinforcement learning to guide parameter selection during model fusion, enabling a more intelligent and adaptive exploration of the parameter space. Additionally, RL–Fusion introduces a dynamic evaluation mechanism that adjusts the evaluation dataset in real-time based on feedback from SOTA models, ensuring continuous enhancement of domain-specific capabilities. RL–Fusion outperforms the baseline model by improving 1.75% in the MMLU benchmark test, 1.8% in the C-eval test, and 1.8% in the Chinese Named Entity Recognition (NER) test on the Yayi NER dataset by 16%. The results show that RL–Fusion is an effective and scalable model fusion solution that improves performance without increasing the computational cost of traditional optimization methods and has a wide range of applications in AI research and practice.