Authorship Attribution using Sequential Part-of-Speech Pattern Mining
Bibliographic record
Abstract
Given an anonymous text, automatically attributing a name from a group of known writers is called "Authorship Attribution" (AA).It is a classification problem, and feature extraction techniques are initially applied, followed by the training of a model using a collection of texts whose authors are known.Numerous features, such as lexical, semantic, structural, n-grams, etc., can be used to identify the stylistic characteristics of writers.The authors of this research propose a novel approach to this problem by using sequential pattern mining on part-of-speech (PoS) tags.This paper introduces and discusses the concept of a Part-of-Speech Skip-Gram (PoSSG) that is different from traditional n-gram.A sequential pattern mining algorithm is applied to obtain PoSSG patterns, which are then used for authorship attribution tasks.Experimental studies on two different datasets: novels extracted from Project Gutenberg and Stamatatos06
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".