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2015 | OriginalPaper | Chapter

Author Attribution of Email Messages Using Parse-Tree Features

Authors : Jagadeesh Patchala, Raj Bhatnagar, Sridharan Gopalakrishnan

Published in: Machine Learning and Data Mining in Pattern Recognition

Publisher: Springer International Publishing

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Abstract

Most existing research on authorship attribution uses various types of lexical, syntactic, and structural features for classification. Some of these features are not meaningful for small texts such as email messages. In this paper we demonstrate a very effective use of a syntactic feature of an author’s writing - text’s parse tree characteristics - for authorship analysis of email messages. We define author templates consisting of context free grammar (CFG) production frequencies occurring in an author’s training set of email messages. We then use similar frequencies extracted from a new email message to match against various authors’ templates to identify the best match. We evaluate our approach on Enron email dataset and show that CFG production frequencies work very well and are robust in attributing the authorship of email messages.

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Metadata
Title
Author Attribution of Email Messages Using Parse-Tree Features
Authors
Jagadeesh Patchala
Raj Bhatnagar
Sridharan Gopalakrishnan
Copyright Year
2015
Book
Machine Learning and Data Mining in Pattern Recognition

Print ISBN: 978-3-319-21023-0

Electronic ISBN: 978-3-319-21024-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-21024-7_21

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