Compared with English named entity recognition (NER), Chinese NER faces significant challenges due to the flexible, non-standard word formation and vague word boundaries, which cause a lot of boundary ambiguity and reduce the accuracy of entity identification. To address this issue, we propose a boundary enhancement with multi-class information model (BEMCI). The model integrates multiple types of information into text embedding while enhancing the subsequent syntax-structure information. A syntactic information analysis module is designed to highlight important syntax information from three aspects, namely part-of-speech tags, syntactic constituents, and dependency relations, to analyze sentence structures. Meanwhile, an improved contextual attention mechanism, which combines contextual and syntactic information using a gate mechanism to control the weight fusion, is proposed to further enhance the model’s boundary determination. Multiple sets of experiments conducted on six general datasets show that BEMCI outperforms other baselines, achieving the best results in four of these six datasets.

The text-to-SQL task aims to convert natural language questions into corresponding SQL queries based on a given database schema. Previous models that rely on graph neural networks often struggle to accurately capture the complex grammatical relationships present in these questions, leading to poor performance when generating queries for longer requests. To address these challenges, we propose RGISQL, which integrates refined grammatical information extracted from the question and employs segmentation processing to effectively manage long queries. Additionally, RGISQL minimizes the complexity of edge embeddings by reducing the coupling within graph neural networks. By utilizing grammatical dependency trees, RGISQL is better equipped to capture the inherent structure and grammatical rules of questions. This refined grammatical information offers additional contextual and semantic cues for the model, thereby enhancing both its generalizability and interpretability. Furthermore, we dynamically assess the importance of different edges based on the graph structure, which helps reduce the coupling of edge embeddings and further improves the model’s performance. Multiple sets of experiments conducted on the Spider and Spider-Syn datasets demonstrate that RGISQL outperforms other baselines, achieving the best results in both datasets.