Natural language to SQL (NL2SQL) provides an intuitive interface for querying structured data, yet real user questions are often noisy, ambiguous, and weakly grounded to database semantics.As a result, token-level schema linking and single-pass SQL decoding can be brittle: small misunderstandings in language or schema grounding may propagate into incorrect generation.We present QBridge, an agentic, feedback-driven NL2SQL framework based on a Refined Gold Query Paradigm, which bridges natural language and SQL via Gold Query-a structured, SQL-aligned intermediate representation.A core insight of QBridge is Distilled Back-Translation (DBT) for SL-independent rewriting.DBT converts SQL-grounded supervision into execution-verified Gold-Querystyle rewrites from a teacher model, and distills a lightweight, plug-and-play rewriter that generates schema-aware rewrites without requiring explicit schema linking at inference.QBridge then (i) verifies and conservatively refines the rewrite into a high-fidelity Refined Gold Query, and (ii) refines the generated SQL with dual feedback from execution validity and semantic consistency, enabling interpretable self-correction while remaining compatible with diverse SQL backbones.Extensive experiments on Spider, BIRD, and three robustness variants demonstrate that QBridge consistently improves zero-shot NL2SQL, outperforming strong prompting and agentic baselines while showing strong robustness and generalization.Code and data are available at https: //github.com/WannaBSteve/QBridge.

Semantic error checking is a weak point of automatic proofreading for Chinese texts, with few mature methods at present. The paper discusses the technology of semantic error detection for Chinese texts, proposes a strategy combining statistical and rules approaches, and uses the collocation relationship based on instances, statistics and rules to check errors. The strategy checks both local semantic restraints and remote semantic collocations, and achieves satisfactory results.

The processing of Chinese names is important to the approach of Chinese word segmentation and automatic abstraction. In this paper we put forward an inverse name frequency model. Based on this model, context pattern, adjacent chain, special name table and position dependent information, we designed an effective system for automatically identifying Chinese names in texts. This paper describes the algorithm of this system, and the experiment result shows its upper recall and precision rate. Its recall rate reaches 93.75% and precision rate reaches 83.95%.

Semantic paragraph partition is an important problem in text structure analysis in an automatic abstracting system. For an article containing distinct headings, the paper presents heading models in Chinese text to divide an article into semantic paragraphs based on the recognition of headings. For an article not containing headings, the paper establishes a vector space model for the whole article based on paragraphs, and then semantically relative paragraphs are clustered as semantic paragraphs.