Similarity Ranking as Attribute for Machine Learning Approach to Authorship Identification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F12%3A00060279" target="_blank" >RIV/00216224:14330/12:00060279 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Similarity Ranking as Attribute for Machine Learning Approach to Authorship Identification

Popis výsledku v původním jazyce

In the authorship identification task, examples of short writings of N authors and an anonymous document written by one of these N authors are given. The task is to determine the authorship of the anonymous text. Practically all approaches solved this problem with machine learning methods. The input attributes for the machine learning process are usually formed by stylistic or grammatical properties of individual documents or a defined similarity between a document and an author. In this paper, we present the results of an experiment to extend the machine learning attributes by ranking the similarity between a document and an author: we transform the similarity between an unknown document and one of the N authors to the order in which the author is themost similar to the document in the set of N authors. The comparison of similarity probability and similarity ranking was made using the Support Vector Machines algorithm.

Název v anglickém jazyce

Similarity Ranking as Attribute for Machine Learning Approach to Authorship Identification

Popis výsledku anglicky

In the authorship identification task, examples of short writings of N authors and an anonymous document written by one of these N authors are given. The task is to determine the authorship of the anonymous text. Practically all approaches solved this problem with machine learning methods. The input attributes for the machine learning process are usually formed by stylistic or grammatical properties of individual documents or a defined similarity between a document and an author. In this paper, we present the results of an experiment to extend the machine learning attributes by ranking the similarity between a document and an author: we transform the similarity between an unknown document and one of the N authors to the order in which the author is themost similar to the document in the set of N authors. The comparison of similarity probability and similarity ranking was made using the Support Vector Machines algorithm.

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

Proceedings of the Eight International Conference on Language Resources and Evaluation

ISBN

9782951740877

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

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Název nakladatele

European Language Resources Association

Místo vydání

Istanbul (Turkey)

Místo konání akce

Istanbul (Turkey)

Datum konání akce

23. 5. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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