Authorship Attribution: Comparison of Single-layer and Double-layer Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F12%3A00060281" target="_blank" >RIV/00216224:14330/12:00060281 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-32790-2_34" target="_blank" >http://dx.doi.org/10.1007/978-3-642-32790-2_34</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-32790-2_34" target="_blank" >10.1007/978-3-642-32790-2_34</a>
Alternative languages
Result language
angličtina
Original language name
Authorship Attribution: Comparison of Single-layer and Double-layer Machine Learning
Original language description
In the traditional authorship attribution task, forensic linguistic specialists analyse and compare documents to determine who was their (real) author. In the current days, the number of anonymous docu- ments is growing ceaselessly because of Internet expansion. That is why the manual part of the authorship attribution process needs to be replaced with automatic methods. Specialized algorithms (SA) like delta-score and word length statistic were developed to quantify the similarity between documents, but currently prevailing techniques build upon the machine learning (ML) approach. In this paper, two machine learning approaches are compared: Single-layer ML, where the results of SA (similarities of documents) are used as input attributes for the machine learning, and Double-layer ML with the numerical information characterizing the author being extracted from documents and divided into several groups.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
AI - Linguistics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/VF20102014003" target="_blank" >VF20102014003: Natural Language Analysis in the Internet Environment</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Text, Speech and Dialogue - 15th International Conference
ISBN
9783642327896
ISSN
0302-9743
e-ISSN
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Number of pages
8
Pages from-to
282-289
Publisher name
Springer
Place of publication
Brno
Event location
Brno, Czech Republic
Event date
Sep 3, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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