用字符分布和位置信号实现轻量级罗马尼亚语作者归属，无需分词或预训练模型。
CHiPS: Character Histograms and Positional Signals for Lightweight Authorship Attribution in Romanian Texts
- 基于单字符分布和字符位置信号构建双特征分类器
- 在严格防泄露条件下达到0.9310准确率与0.9341宏F1
- 适合关注可解释性与低资源作者识别的研究者
我们提出CHiPS，一种面向罗马尼亚语文本的轻量级字符级作者归属方法。所有实验均为封闭集：真实作者必在训练数据候选集中。CHiPS研究两种互补的写作指纹：基于单字符边缘分布的字符直方图分类器（CH-SVM），以及将特定字符与标点类别表示为脉冲序列（位置二值指示序列）并提取傅里叶/韦尔奇谱描述符的位置信号分类器（FFT12-LR）。还报告了泄漏安全的决策级融合变体CHiPS-F，以及仅使用交叉验证预测训练的可选前5名列表重排序器。该方法无需分词、句法分析、预训练语言模型或Transformer微调，且直方图组件避免使用字符n-gram特征（n≥2）。在包含400个文件、来自392个源文本组、10位作者的锁定分组ROST划分上，采用源文本级评估与分组五折模型选择，CHiPS-F达到0.9310准确率与0.9341宏F1。相同测试集下，不受限的字符2–5-gram TF-IDF SVM达到1.0000准确率与宏F1，因此本工作的贡献不在于最高分类精度，而在于探究在严格防泄露条件下，受限、透明的字符证据能达到何种程度。在另一组重叠于ROST的清洁数据集ROStories-cleaned（含1,248个文件、1,240个源文本组、19位作者）上，相同协议下CHiPS-R达0.8919准确率与0.8708宏F1。
原文摘要 · Abstract (English)
We propose CHiPS, a lightweight character-level authorship attribution method for Romanian texts. All reported experiments are closed-set: the true author is one of the candidate authors in the training data. CHiPS studies two complementary fingerprints of writing style: CH-SVM, a character-histogram classifier based on one-character marginal distributions, and FFT12-LR, a positional-signal classifier that represents selected characters and punctuation classes as impulse trains (binary indicator sequences over character positions) and extracts Fourier/Welch spectral descriptors. We also report CHiPS-F, a leakage-safe decision-level fusion variant, and an optional top-5 listwise reranker trained only on out-of-fold predictions. The method requires no tokenization, syntactic analysis, pretrained language model, or transformer fine-tuning, and it avoids character $n$-gram features with $n \geq 2$ in the histogram component. On a locked grouped ROST split comprising 400 files from 392 source-text groups, written by 10 authors, with source-text-level evaluation and grouped five-fold model selection, CHiPS-F reaches 0.9310 accuracy and 0.9341 macro-F1. A matched but unrestricted character 2--5-gram TF--IDF SVM comparator reaches 1.0000 accuracy and macro-F1 on the same held-out groups, so the contribution is not a claim of best possible classification accuracy. Instead, the experiments ask how far restricted, transparent character evidence can go under strict leakage control. On ROSTories-cleaned, a secondary ROST-overlapping corpus comprising 1,248 files from 1,240 source-text groups, written by 19 authors, the same protocol gives 0.8919 accuracy and 0.8708 macro-F1 for CHiPS-R.
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