Download e-book for kindle: Authorship Attribution by Patrick Juola

By Patrick Juola

Authorship attribution, the technological know-how of inferring features of the writer from the features of files written through that writer, is an issue with a protracted heritage and quite a lot of software. it really is an incredible challenge not just in details retrieval yet in lots of different disciplines besides, from know-how to educating and from finance to forensics. the concept authors have a statistical "fingerprint'' that may be detected through desktops is a compelling one who has obtained loads of learn cognizance. Authorship Attribution surveys the background and current kingdom of the self-discipline, proposing a few comparative effects the place to be had. It additionally offers a theoretical and empirically-tested foundation for extra paintings. Many glossy strategies are defined and evaluated, in addition to a few insights for software for rookies and specialists alike. Authorship Attribution can be of specific curiosity to info retrieval researchers and scholars who are looking to stay alongside of the newest ideas and their functions. it's also an invaluable source for individuals in different disciplines, be it the trainer drawn to plagiarism detection or the historian drawn to who wrote a specific record.

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The most sophisticated (and psychologically plausible) are context-free grammars (CFGs) and their extensions. In general a context-free grammar [65] is a set of rewrite rules that permit abstract symbols (typi21 22 Linguistic and Mathematical Background cally representing grammatical categories) to be re-written as strings of other categories and specific words. For example, (in English) a prepositional phrase (usually symbolized/abbreviated as P P ) might be rewritten (→) as a preposition (P REP ) followed by a noun phrase (N P ).

A key problem with vector space models can be the number of dimensions and the concomitant computational burden. For this reason, vector space models are often used in conjunction with one or more methods of factor analysis to reduce the number of variables. This analysis serves two purposes: first, to identify dependencies and correlations within the different vectors, and second, to identify the most salient dimensions of variation within the vector space. Two typical types of factor analysis are principal components analysis (PCA) and linear discriminant analysis (LDA).

David Hoover [63, 64] has made extensive study of such variations. Examples of the variations that he has studied include changing the number of words studied (ranging from 20 to 800 and beyond), eliminating contractions and/or personal pronouns from the set of wordvariables, and “culling” the list of word-variables by eliminating words for which a single training document supplied most (70% [64]) of the words. He found the greatest accuracy occurred in a 700-dimensional space, eliminating personal pronouns but not contractions, and applying culling at the 70% level.

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