Author attribution on paragraph level using simulated annealing

M.P. Schraagen; D.K.W. van Miert

Author attribution on paragraph level using simulated annealing

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

Abstract

Authorship attribution, i.e., determining the author of a document automatically based on a reference corpus, is an established topic in computational linguistics and Digital Humanities. However, state of the art techniques are generally applied to documents of 5000 words or more. This research explores a method to attribute authorship on paragraph level, using simulated annealing to incrementally increase the accuracy of classification.

Original language	English
Title of host publication	Proceedings of Benelearn 2016
Number of pages	3
Publication status	Published - Sept 2016
Event	The 25th Belgian-Dutch Conference on Machine Learning (Benelearn) - Katholieke Universiteit Leuven, Kortrijk, Belgium Duration: 12 Sept 2016 → 13 Sept 2016 https://www.kuleuven-kulak.be/benelearn/

Conference

Conference	The 25th Belgian-Dutch Conference on Machine Learning (Benelearn)
Country/Territory	Belgium
City	Kortrijk
Period	12/09/16 → 13/09/16
Internet address	https://www.kuleuven-kulak.be/benelearn/

Keywords

author attribution
Enlightenment philosophy
simulated annealing

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Benelearn_2016_paper_44Final published version, 278 KB

Cite this

@inproceedings{b7df50812f5846549c7bc3ea1b9a11f1,

title = "Author attribution on paragraph level using simulated annealing",

abstract = "Authorship attribution, i.e., determining the author of a document automatically based on a reference corpus, is an established topic in computational linguistics and Digital Humanities. However, state of the art techniques are generally applied to documents of 5000 words or more. This research explores a method to attribute authorship on paragraph level, using simulated annealing to incrementally increase the accuracy of classification.",

keywords = "author attribution, Enlightenment philosophy, simulated annealing",

author = "M.P. Schraagen and {van Miert}, D.K.W.",

year = "2016",

month = sep,

language = "English",

booktitle = "Proceedings of Benelearn 2016",

note = "The 25th Belgian-Dutch Conference on Machine Learning (Benelearn) ; Conference date: 12-09-2016 Through 13-09-2016",

url = "https://www.kuleuven-kulak.be/benelearn/",

}

TY - GEN

T1 - Author attribution on paragraph level using simulated annealing

AU - Schraagen, M.P.

AU - van Miert, D.K.W.

PY - 2016/9

Y1 - 2016/9

N2 - Authorship attribution, i.e., determining the author of a document automatically based on a reference corpus, is an established topic in computational linguistics and Digital Humanities. However, state of the art techniques are generally applied to documents of 5000 words or more. This research explores a method to attribute authorship on paragraph level, using simulated annealing to incrementally increase the accuracy of classification.

AB - Authorship attribution, i.e., determining the author of a document automatically based on a reference corpus, is an established topic in computational linguistics and Digital Humanities. However, state of the art techniques are generally applied to documents of 5000 words or more. This research explores a method to attribute authorship on paragraph level, using simulated annealing to incrementally increase the accuracy of classification.

KW - author attribution

KW - Enlightenment philosophy

KW - simulated annealing

M3 - Conference contribution

BT - Proceedings of Benelearn 2016

T2 - The 25th Belgian-Dutch Conference on Machine Learning (Benelearn)

Y2 - 12 September 2016 through 13 September 2016

ER -

Author attribution on paragraph level using simulated annealing

Abstract

Conference

Keywords

Access to Document

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Cite this