Authorship identification in large email collections: Experiments using features that belong to different linguistic levels Notebook for PAN at CLEF 2011

George K. Mikros; Kostas Perifanos

Authorship identification in large email collections: Experiments using features that belong to different linguistic levels Notebook for PAN at CLEF 2011

George K. Mikros, Kostas Perifanos

National and Kapodistrian University of Athens

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

The aim of this paper is to explore the usefulness of using features from different linguistic levels to email authorship identification. Using various email datasets provided by PAN'11 lab we tested several feature groups in both authorship attribution and authorship verification subtasks. The selected feature groups combined with Regularized Logistic Regression and One-Class SVM machine learning methods performed well above average in authorship attribution subtasks and below average in authorship verification subtasks.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	1177
Publication status	Published - 2011
Externally published	Yes
Event	2011 Cross Language Evaluation Forum Conference, CLEF 2011 - Amsterdam, Netherlands Duration: 19 Sept 2011 → 22 Sept 2011

Keywords

Authorship attribution
Authorship verification
LIBLINEAR
LIBSVM
One-class SVM
Regularized logistic regression
Stylometry
Support vector machines

Cite this

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title = "Authorship identification in large email collections: Experiments using features that belong to different linguistic levels Notebook for PAN at CLEF 2011",

abstract = "The aim of this paper is to explore the usefulness of using features from different linguistic levels to email authorship identification. Using various email datasets provided by PAN'11 lab we tested several feature groups in both authorship attribution and authorship verification subtasks. The selected feature groups combined with Regularized Logistic Regression and One-Class SVM machine learning methods performed well above average in authorship attribution subtasks and below average in authorship verification subtasks.",

keywords = "Authorship attribution, Authorship verification, LIBLINEAR, LIBSVM, One-class SVM, Regularized logistic regression, Stylometry, Support vector machines",

author = "Mikros, {George K.} and Kostas Perifanos",

year = "2011",

language = "English",

volume = "1177",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

note = "2011 Cross Language Evaluation Forum Conference, CLEF 2011 ; Conference date: 19-09-2011 Through 22-09-2011",

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T2 - 2011 Cross Language Evaluation Forum Conference, CLEF 2011

AU - Mikros, George K.

AU - Perifanos, Kostas

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N2 - The aim of this paper is to explore the usefulness of using features from different linguistic levels to email authorship identification. Using various email datasets provided by PAN'11 lab we tested several feature groups in both authorship attribution and authorship verification subtasks. The selected feature groups combined with Regularized Logistic Regression and One-Class SVM machine learning methods performed well above average in authorship attribution subtasks and below average in authorship verification subtasks.

AB - The aim of this paper is to explore the usefulness of using features from different linguistic levels to email authorship identification. Using various email datasets provided by PAN'11 lab we tested several feature groups in both authorship attribution and authorship verification subtasks. The selected feature groups combined with Regularized Logistic Regression and One-Class SVM machine learning methods performed well above average in authorship attribution subtasks and below average in authorship verification subtasks.

KW - Authorship attribution

KW - Authorship verification

KW - LIBLINEAR

KW - LIBSVM

KW - One-class SVM

KW - Regularized logistic regression

KW - Stylometry

KW - Support vector machines

UR - http://www.scopus.com/inward/record.url?scp=84922042082&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84922042082

SN - 1613-0073

VL - 1177

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 19 September 2011 through 22 September 2011

ER -

Authorship identification in large email collections: Experiments using features that belong to different linguistic levels Notebook for PAN at CLEF 2011

Abstract

Keywords

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