用提示工程实现中文网络新词自动识别，精准捕捉语言演化现象。
Operationalizing Linguistic Methods through Prompt-Engineering Skills: An Automatic Chinese Web Neologism Detection Pipeline
- 将语言学规则转为提示技能，分四步自动检测新词。
- 在2.67亿文档中发现4853个新词，准确率受长度影响显著。
- 适合研究网络语言、语义演化或提示工程的学者使用。
我们提出一种自动识别中文网络新词的方法，将传统语言学识别原则转化为提示工程技能。该方法包含四个阶段：不依赖分词器的字符n-gram候选生成；基于点互信息的词典锚定预筛选；基于汉语构词规律的合法性判断；以及结合规则与三分类模型的语义新颖性判别，区分新词、实体和无关词。应用于BAAI CCI 3.0语料库（26700万文档），共识别出226,959个候选词，其中4,853个被标注为新词。通过阶段条件召回分解评估，发现第一阶段候选覆盖和第四阶段大模型语义判断是两大瓶颈（召回率分别为41.5%和60.0%），中间阶段几乎无损失。长度分层分析显示，构词合法性技能对长度不敏感（≥96.9%），而语义新颖性判别则随长度下降（2/3/4字候选召回率分别为65.6%/59.0%/44.1%），揭示了当前技能化语言操作的边界。方法、输出及评估协议已公开。
原文摘要 · Abstract (English)
We present a method for automatic Chinese web neologism detection that operationalizes traditional linguistic identification principles as prompt-engineering skills. The method has four stages: tokenizer-independent character n-gram candidate generation; dictionary anchoring with a Pointwise Mutual Information pre-filter; a well-formedness skill based on Chinese word-formation principles; and a combined rule and three-way classification skill that distinguishes neologism, entity, and none. Applied to the BAAI CCI 3.0 corpus (267M documents), the method produces 226,959 classified candidates including 4,853 labeled neologisms. To evaluate the method, we develop a per-stage conditional recall decomposition in which the pipeline's strict recall factors mathematically into the product of stage conditional recalls. Applied to Hou (2023) (4,199 entries), the decomposition exposes Stage 1 candidate coverage and Stage 4B LLM semantic judgment as the two bottlenecks (R=41.5% and 60.0% respectively), while intermediate stages are near-lossless. A length-stratified analysis further reveals that the structural well-formedness skill is length-invariant (>= 96.9%) whereas the semantic novelty-classification skill is length-dependent (65.6%/59.0%/44.1% across 2/3/4-character candidates), mapping a current boundary of skill-based linguistic operationalization. We release the method, pipeline outputs, and evaluation protocol as public resources.
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