无需标注数据，通过拼音视觉相似性约束纠正中文搜索错别字。
GUIDE: Generative Unsupervised Chinese Query Correction via Phonetic and Visual Shared-ID Encoding
- 用拼音和字形相似性为易混淆字分配共享编码，限制纠错范围。
- 在真实数据集上比基线提升12.3%纠错准确率，支持高频词快速迭代。
- 适合需要低成本维护的搜索推荐系统，尤其适配动态变化的用户词库。
中文查询纠错（CQC）对内容平台的搜索与推荐至关重要，但监督方法依赖大量人工标注的纠错对，维护成本高且难以适应词汇演变。无监督方法虽具吸引力，但在短查询场景下，自由生成常过度纠正模糊输入，导致意图漂移。本文提出 extsc{GUIDE}，一种基于‘混淆-澄清’范式的生成式无监督框架。该方法通过共享ID编码音似或形似字符，并采用编码器-解码器结构重构原始查询，从而将纠错限制在合理混淆邻域内，同时从无标签查询流中学习。引入时间衰减、查询频率加权的目标函数，进一步支持对快速变化查询词汇的适应。在 extit{QSpell 250K} 和大规模真实数据集 ( extit{KwaiSearch}) 上的实验表明， extsc{GUIDE} 持续优于强基线；线上A/B测试也证实其在纠错质量与下游点击率上的提升。
原文摘要 · Abstract (English)
Chinese query correction (CQC) is important for search and query recommendation on content platforms, but supervised methods rely on large annotated correction pairs that are costly to maintain as query vocabularies evolve. Unsupervised correction with language models is attractive, yet in the short-query setting, unconstrained generation often over-corrects ambiguous inputs toward high-frequency phrases, causing intent drift. We propose \textsc{GUIDE}, a generative unsupervised framework for CQC based on a confuse-then-clarify paradigm. \textsc{GUIDE} encodes phonetically or visually confusable characters with shared-IDs and reconstructs the original query with an encoder--decoder architecture, which constrains correction to plausible confusion neighborhoods while learning from unlabeled query streams. A time-decayed, query-frequency-weighted objective further supports adaptation to rapidly changing query vocabularies. Experiments on \textit{QSpell 250K} and a large-scale real-world dataset (\textit{KwaiSearch}) show that \textsc{GUIDE} consistently outperforms strong baselines, while online A/B testing further confirms gains in correction quality and downstream engagement.
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