Authorship Attribution: Comparison of Single-layer and Double-layer Machine Learning

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Authorship Attribution: Comparison of Single-layer and Double-layer Machine Learning

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Authorship Attribution: Comparison of Single-layer and Double-layer Machine Learning

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

AI - Jazykověda

OECD FORD obor

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Projekt

<a href="/cs/project/VF20102014003" target="_blank" >VF20102014003: Analýza přirozeného jazyka v prostředí internetu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Text, Speech and Dialogue - 15th International Conference

ISBN

9783642327896

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

8

Strana od-do

282-289

Název nakladatele

Springer

Místo vydání

Brno

Místo konání akce

Brno, Czech Republic

Datum konání akce

3. 9. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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