Improving cross-topic authorship attribution: The role of pre-processing

I. Markov; E. Stamatatos; G. Sidorov

doi:10.1007/978-3-319-77116-8_21

Improving cross-topic authorship attribution: The role of pre-processing

I. Markov, E. Stamatatos, G. Sidorov

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

© Springer Nature Switzerland AG 2018.The effectiveness of character n-gram features for representing the stylistic properties of a text has been demonstrated in various independent Authorship Attribution (AA) studies. Moreover, it has been shown that some categories of character n-grams perform better than others both under single and cross-topic AA conditions. In this work, we present an improved algorithm for cross-topic AA. We demonstrate that the effectiveness of character n-grams representation can be significantly enhanced by performing simple pre-processing steps and appropriately tuning the number of features, especially in cross-topic conditions.

Original language	English
Title of host publication	Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers
Editors	A. Gelbukh
Publisher	Springer Verlag
Pages	289-302
ISBN (Print)	9783319771151
DOIs	https://doi.org/10.1007/978-3-319-77116-8_21
Publication status	Published - 2018
Externally published	Yes
Event	18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017 - Budapest, Hungary Duration: 17 Apr 2017 → 23 Apr 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017
Country/Territory	Hungary
City	Budapest
Period	17/04/17 → 23/04/17

Funding

Acknowledgments. This work was partially supported by the Mexican Government (CONACYT projects 240844, SNI, COFAA-IPN, SIP-IPN 20161947, 20161958, 20162204, 20162064, 20171813, 20171344, and 20172008). This work was partially supported by the Mexican Government (CONACYT projects 240844, SNI, COFAA-IPN, SIP-IPN 20161947, 20161958, 20162204, 20162064, 20171813, 20171344, and 20172008).

Funders	Funder number
Mexican Government
Instituto Politécnico Nacional	20161947
Consejo Nacional de Ciencia y Tecnología	240844
Secretaría de Investigación y Posgrado, Instituto Politécnico Nacional
Comisión de Operación y Fomento de Actividades Académicas, Instituto Politécnico Nacional
Sistema Nacional de Investigadores	20162204, 20171344, 20172008, 20162064, 20171813, 20161958, SIP-IPN 20161947

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10.1007/978-3-319-77116-8_21

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Markov, I., Stamatatos, E., & Sidorov, G. (2018). Improving cross-topic authorship attribution: The role of pre-processing. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers (pp. 289-302). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Springer Verlag. https://doi.org/10.1007/978-3-319-77116-8_21

Markov, I. ; Stamatatos, E. ; Sidorov, G. / Improving cross-topic authorship attribution : The role of pre-processing. Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. editor / A. Gelbukh. Springer Verlag, 2018. pp. 289-302 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "{\textcopyright} Springer Nature Switzerland AG 2018.The effectiveness of character n-gram features for representing the stylistic properties of a text has been demonstrated in various independent Authorship Attribution (AA) studies. Moreover, it has been shown that some categories of character n-grams perform better than others both under single and cross-topic AA conditions. In this work, we present an improved algorithm for cross-topic AA. We demonstrate that the effectiveness of character n-grams representation can be significantly enhanced by performing simple pre-processing steps and appropriately tuning the number of features, especially in cross-topic conditions.",

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Markov, I, Stamatatos, E & Sidorov, G 2018, Improving cross-topic authorship attribution: The role of pre-processing. in A Gelbukh (ed.), Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, pp. 289-302, 18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017, Budapest, Hungary, 17/04/17. https://doi.org/10.1007/978-3-319-77116-8_21

Improving cross-topic authorship attribution: The role of pre-processing. / Markov, I.; Stamatatos, E.; Sidorov, G.
Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. ed. / A. Gelbukh. Springer Verlag, 2018. p. 289-302 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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N2 - © Springer Nature Switzerland AG 2018.The effectiveness of character n-gram features for representing the stylistic properties of a text has been demonstrated in various independent Authorship Attribution (AA) studies. Moreover, it has been shown that some categories of character n-grams perform better than others both under single and cross-topic AA conditions. In this work, we present an improved algorithm for cross-topic AA. We demonstrate that the effectiveness of character n-grams representation can be significantly enhanced by performing simple pre-processing steps and appropriately tuning the number of features, especially in cross-topic conditions.

AB - © Springer Nature Switzerland AG 2018.The effectiveness of character n-gram features for representing the stylistic properties of a text has been demonstrated in various independent Authorship Attribution (AA) studies. Moreover, it has been shown that some categories of character n-grams perform better than others both under single and cross-topic AA conditions. In this work, we present an improved algorithm for cross-topic AA. We demonstrate that the effectiveness of character n-grams representation can be significantly enhanced by performing simple pre-processing steps and appropriately tuning the number of features, especially in cross-topic conditions.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Markov I, Stamatatos E, Sidorov G. Improving cross-topic authorship attribution: The role of pre-processing. In Gelbukh A, editor, Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers. Springer Verlag. 2018. p. 289-302. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-77116-8_21

Improving cross-topic authorship attribution: The role of pre-processing

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