2026-07-28 04:00 UTCOriginal source2 min readUpdated: 2026-07-28 08:45 UTC

CHiPS: Character Histograms and Positional Signals for Lightweight Authorship Attribution in Romanian Texts

Researchers propose CHiPS, a lightweight character-level authorship attribution method for Romanian texts. It combines a character histogram classifier (CH-SVM) and a positional signal classifier (FFT12-LR), requiring no tokenization, syntactic analysis, pretrained language models, or transformer fine-tuning. In closed-set experiments, CHiPS-F achieves 0.9310 accuracy on a 400-file dataset, but the authors emphasize the contribution is not about achieving the best classification performance, but rather exploring how far restricted, transparent character evidence can go under strict leakage control.

SourcearXiv Computational LinguisticsAuthor: Sanda-Maria Avram, George C. \c{T}urca\c{s}

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[Submitted on 24 Jul 2026]

Title:CHiPS: Character Histograms and Positional Signals for Lightweight Authorship Attribution in Romanian Texts

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Abstract: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.

Comments: 17 pages, 12 tables

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2607.22884 [cs.CL]

(or arXiv:2607.22884v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.22884

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: George Cătălin Ţurcaş [view email] [v1] Fri, 24 Jul 2026 19:48:32 UTC (34 KB)

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