汉语中介语语料库“AI助手”辅助检索功能的模拟与可行性初探

Simulation and Preliminary Feasibility Study of “AI Assistant” Retrieval Functions in Chinese Interlanguage Corpora

摘要: 当前主流汉语中介语语料库的检索系统多依赖“字符串一般检索”及“分类标注检索”等模块，存在操作逻辑割裂、多条件组合困难、典型偏误排序缺失等实际痛点。本文基于社交平台收集的语料库使用者反馈，发现大量用户在“理解偏误标注代码”、“进行复杂语法条件检索”以及“查看错字原貌”等方面存在普遍焦虑。借鉴电商平台“AI助手”的自然语言交互逻辑，本研究在不具备后台改造权限的前提下，以实际下载的300条“把字句”中介语语料为实验对象，通过设计特定提示词(Prompt)，对大模型的“意图解析、跨维度语义召回、典型度推荐”等能力进行了实证模拟。实验发现，AI助手能有效破解传统搜索页面的功能限制，并针对AI误判提出了“强制匹配偏误标注符”的人机协同约束策略。本研究验证了轻量化AI助手模式在语料库智能化升级中的实际应用价值。

Abstract: Most retrieval systems for mainstream Chinese interlanguage corpora mainly adopt modules such as general string retrieval and categorical annotation retrieval, which are plagued by practical drawbacks including disjoint operational logic, cumbersome multi-condition combination, and the absence of ranking mechanisms for typical errors. Based on feedback collected from corpus users via social media platforms, this paper identifies widespread user frustrations regarding the interpretation of error annotation codes, execution of complex grammatical conditional retrieval, and access to the original forms of miswritten characters. Drawing on the natural language interaction logic of AI assistants deployed on e-commerce platforms, this study takes 300 downloaded interlanguage corpus entries of the ba-construction as experimental materials without access to backend system modification privileges. By designing dedicated Prompts, it conducts an empirical simulation of large language models’ capabilities in intent parsing, cross-dimensional semantic recall and typicality recommendation. Experiments verify that the AI assistant can effectively break through the functional constraints of conventional search interfaces. To address AI misclassification errors, a human-machine collaborative constraint strategy featuring forced matching of error annotation markers is proposed. This research confirms the practical applicability of the lightweight AI assistant model for the intelligent upgrading of Chinese interlanguage corpora.