An N-gram based distributional test for authorship identification

Fragos, K; Skourlas, C

dc.contributor.author	Fragos, K	en
dc.contributor.author	Skourlas, C	en
dc.date.accessioned	2014-03-01T02:43:55Z
dc.date.available	2014-03-01T02:43:55Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31553
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-77954117086&partnerID=40&md5=42520e935fd87ae59fef7c8180b74056	en
dc.relation.uri	http://www.informatik.uni-trier.de/~ley/db/conf/nlucs/nlucs2006.html#FragosS06a	en
dc.subject.other	Authorship attribution	en
dc.subject.other	Authorship identification	en
dc.subject.other	Character level	en
dc.subject.other	Feature selection	en
dc.subject.other	Kolmogorov-Smirnov test	en
dc.subject.other	N-grams	en
dc.subject.other	NLP tools	en
dc.subject.other	Novel methods	en
dc.subject.other	Test sets	en
dc.subject.other	Text collection	en
dc.subject.other	Text preprocessing	en
dc.subject.other	Text segmentation	en
dc.subject.other	Training data	en
dc.subject.other	Competition	en
dc.subject.other	Feature extraction	en
dc.subject.other	Query languages	en
dc.subject.other	Linguistics	en
dc.title	An N-gram based distributional test for authorship identification	en
heal.type	conferenceItem	en
heal.publicationDate	2006	en
heal.abstract	In this paper, a novel method for the authorship identification problem is presented. Based on character level text segmentation we study the disputed text's N-grams distributions within the authors' text collections. The distribution that behaves most abnormally is identified using the Kolmogorov -Smirnov test and the corresponding Author is selected as the correct one. Our method is evaluated using the test sets of the 2004 ALLC/ACH Ad-hoc Authorship Attribution Competition and its performance is comparable with the best performances of the participants in the competition. The main advantage of our method is that it is a simple, not parametric way for authorship attribution without the necessity of building authors' profiles from training data. Moreover, the method is language independent and does not require segmentation for languages such as Chinese or Thai. There is also no need for any text preprocessing or higher level processing, avoiding thus the use of taggers, parsers, feature selection strategies, or the use of other language dependent NLP tools.	en
heal.journalName	Proceedings of the 3rd International Workshop on Natural Language Understanding and Cognitive Science, NLUCS 2006, in Conjunction with ICEIS 2006	en
dc.identifier.spage	139	en
dc.identifier.epage	148	en