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A likelihood ratio-based evaluation of strength of authorship attribution evidence in SMS messages using N-grams

Ishihara, Shunichi

Description

An experiment in forensic text comparison (FTC) within the likelihood ratio (LR) framework is described. The experiment attempts to determine the strength of author- ship attribution evidence modelled with N-grams, which is perhaps one of the most basic automatic modelling techniques. The SMS messages of multiple authors selected from the SMS corpus compiled by the National University of Singapore were used for same- and different-author comparisons. The number of words used for the N-gram...[Show more]

dc.contributor.authorIshihara, Shunichi
dc.date.accessioned2015-12-13T22:27:19Z
dc.date.available2015-12-13T22:27:19Z
dc.identifier.issn1748-8893
dc.identifier.urihttp://hdl.handle.net/1885/73894
dc.description.abstractAn experiment in forensic text comparison (FTC) within the likelihood ratio (LR) framework is described. The experiment attempts to determine the strength of author- ship attribution evidence modelled with N-grams, which is perhaps one of the most basic automatic modelling techniques. The SMS messages of multiple authors selected from the SMS corpus compiled by the National University of Singapore were used for same- and different-author comparisons. The number of words used for the N-gram modelling was varied (200, 1000, 2000 or 3000 words), and then the performance of each set was assessed. The performance of the LR-based FTC system was assessed with the log likelihood ratio cost (Cllr). It is shown in this study that N-grams can be employed within an LR framework to discriminate same-author and different-author SMS texts, but a fairly large amount of data are needed to do it well (i.e. to obtain Cllr < 0.75). It is concluded that the LR framework warrants further examination with different features and processing techniques.
dc.publisherEquinox Publishing Ltd
dc.sourceThe International Journal of Speech, Language and the Law
dc.titleA likelihood ratio-based evaluation of strength of authorship attribution evidence in SMS messages using N-grams
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume21
dc.date.issued2014
local.identifier.absfor200300 - LANGUAGE STUDIES
local.identifier.absfor200400 - LINGUISTICS
local.identifier.absfor200402 - Computational Linguistics
local.identifier.ariespublicationU3488905xPUB3877
local.type.statusPublished Version
local.contributor.affiliationIshihara, Shunichi, College of Asia and the Pacific, ANU
local.bibliographicCitation.issue1
local.bibliographicCitation.startpage23
local.bibliographicCitation.lastpage49
local.identifier.doi10.1558/ijsll.v21i1.23
local.identifier.absseo970110 - Expanding Knowledge in Technology
local.identifier.absseo940406 - Legal Processes
local.identifier.absseo940403 - Criminal Justice
dc.date.updated2015-12-11T08:30:32Z
local.identifier.scopusID2-s2.0-84903386913
local.identifier.thomsonID000338781900002
CollectionsANU Research Publications

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