By Patrick Juola
Authorship attribution, the technological know-how of inferring features of the writer from the features of records written by way of that writer, is an issue with an extended background and quite a lot of software. it really is an immense challenge not just in info retrieval yet in lots of different disciplines besides, from expertise to educating and from finance to forensics. the concept that authors have a statistical "fingerprint'' that may be detected through pcs is a compelling one who has obtained loads of examine consciousness. Authorship Attribution surveys the background and current kingdom of the self-discipline, featuring a few comparative effects the place to be had. It additionally presents a theoretical and empirically-tested foundation for extra paintings. Many smooth innovations are defined and evaluated, in addition to a few insights for software for beginners and specialists alike. Authorship Attribution might be of specific curiosity to details retrieval researchers and scholars who are looking to stay alongside of the newest concepts and their purposes. it's also an invaluable source for individuals in different disciplines, be it the instructor attracted to plagiarism detection or the historian attracted to who wrote a specific rfile.
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Extra resources for Authorship Attribution
The mathematics of such statistics is well-known and needs no detailed explanation. 48 Attributional Analysis Unfortunately, these simple methods produce equally simple failures. More accurately, no single feature has been found that robustly separates different authors in a large number of cases. But these simple statistics can be and have been combined successfully. The most notable example of this is Burrows’ “Delta” method [21, 64, 138]. In its original form, Burrows analyzed the frequency of the 150 most frequent words in a collection of Restoration poets.
Performance of Burrows’ Delta has generally been considered to be very good among attribution specialists, and it has in many cases come to represent the baseline against which new methods are compared. Intuitively, Delta can be viewed as creating a 150-dimensional vector space of word frequencies, scaling each dimension by the frequency variation to normalize the degree of dispersion, and embedding individual documents in this space (the analysis to this point is of course unsupervised). He then applies a simple metric to the space (the mean in this case is a simple re-scaling of the L1 -metric) to obtain average distances between the test document and the training documents (by category), selecting the appropriate category based on a variation of the nearest neighbor algorithm (choosing the nearest category instead of document) (see  for further discussion of this analysis).
By their very nature, the analysis and evidence usually associated with these processes is 38 Linguistic Features not of the same type as the soft statistics described elsewhere in this section. At the same time, the potential informativeness of metadata should not be overlooked (and could be considered as a generalization of formatting/layout features described earlier) . m. are two “features” that might tell against a theory of authorship by a notoriously nocturnal Linux advocate, and should be weighed accordingly.