In the context of the rapid development of intelligent education and personalized learning, constructing high-quality curriculum knowledge graphs has become a core task. As a pivotal link in knowledge extraction, named entity recognition (NER) is essential for the construction of knowledge graphs. However, because curriculum texts exhibit deep complexity in type, structure, and semantics, current research faces two critical challenges: recognizing fuzzy entity boundaries and accurately capturing long-distance semantic dependencies. To address these issues, this study proposes MP-CNER, a curriculum-oriented NER model that integrates multi-dimensional position features. The model adopts a dual-branch parallel architecture: one branch utilizes the Chinese-RoBERTa-wwm-ext pre-trained model to extract deep character-level semantic vectors, while the other branch incorporates an innovatively designed multi-dimensional position feature (MDPF) fusion module. Combined with domain dictionaries, this module significantly enhances the model’s perception accuracy regarding entity boundary features. Building on this, a selective contextual linker (SCL) is introduced to leverage boundary signals as guidance, effectively capturing logical associations within long-span entities and across entities. Comprehensive experimental evaluations on three datasets demonstrate that the proposed MP-CNER significantly outperforms existing baseline models in key metrics such as F1 score, fully validating its effectiveness and versatility in processing curriculum texts.
Qiu et al. (Mon,) studied this question.