Author verification using syntactic N-grams

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Abstract

This paper describes our approach to tackle the Author Verification task at PAN 2015. Our method builds a representation of an author's style by using the information contained in dependency trees. This information is represented as syntactic n-grams and used to conform a vector space. Using unsupervised machine learning approach, each instance is associated to the correponding author using the Jaccard distance. In this paper, we describe the features that were used and the employed unsupervised machine learning algorithm.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume1391
StatePublished - 2015
Event16th Conference and Labs of the Evaluation Forum, CLEF 2015 - Toulouse, France
Duration: 8 Sep 201511 Sep 2015

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