Efstathios Stamatatos

Also published as: E. Stamatatos


Authorship Attribution Using Text Distortion
Efstathios Stamatatos
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Authorship attribution is associated with important applications in forensics and humanities research. A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre. In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures. The proposed method attempts to mask topic-specific information that is not related to the personal style of authors. Based on experiments on two main tasks in authorship attribution, closed-set attribution and authorship verification, we demonstrate that the proposed approach can enhance existing methods especially under cross-topic conditions, where the training and test corpora do not match in topic.


Detection of Text Reuse in French Medical Corpora
Eva D’hondt | Cyril Grouin | Aurélie Névéol | Efstathios Stamatatos | Pierre Zweigenbaum
Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)

Electronic Health Records (EHRs) are increasingly available in modern health care institutions either through the direct creation of electronic documents in hospitals’ health information systems, or through the digitization of historical paper records. Each EHR creation method yields the need for sophisticated text reuse detection tools in order to prepare the EHR collections for efficient secondary use relying on Natural Language Processing methods. Herein, we address the detection of two types of text reuse in French EHRs: 1) the detection of updated versions of the same document and 2) the detection of document duplicates that still bear surface differences due to OCR or de-identification processing. We present a robust text reuse detection method to automatically identify redundant document pairs in two French EHR corpora that achieves an overall macro F-measure of 0.68 and 0.60, respectively and correctly identifies all redundant document pairs of interest.


Text Genre Detection Using Common Word Frequencies
E. Stamatatos | N. Fakotakis | G. Kokkinakis
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

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Automatic Text Categorization In Terms Of Genre and Author
Efstathios Stamatatos | Nikos Fakotakis | George Kokkinakis
Computational Linguistics, Volume 26, Number 4, December 2000


Automatic Authorship Attribution
E. Stamatatos | N. Fakotakis | G. Kokkinakis
Ninth Conference of the European Chapter of the Association for Computational Linguistics