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Advanced Models for Stylometric Applications

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Machine Learning Methods for Stylometry

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Abstract

Some well-known models have been explained in the previous chapter, but various advanced approaches have been suggested. Related to the humanities, the Zeta test is focusing on terms used recurrently by one author and mainly ignored by the others. Selecting stylistic markers based on this criterion, the model builds a graph showing the similarities between text excerpts. Compression algorithms could also be applied to identify the true author of a text based on similar word frequencies. More related to the natural language processing domain, the latent Dirichlet allocation (LDA) could be applied to define the most probable author of a given document. To solve the verification problem, several dedicated approaches have been suggested and an overview of them is included in this chapter. Although we usually assume that a novel is written only by a single person, collaborative authorship is possible. To detect passages written by each possible author, the rolling Delta and other ad hoc approaches are described. As neural models constitute an important research field, three sections have been dedicated to them, with one on the basic neural approach, one focusing on word embeddings, and the third on the long short-term memory (LSTM), a well-known deep learning model. The last section is dedicated to adversarial stylometry and obfuscation, or how one can possibly program a computer to hide stylistic markers left by the original author.

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Savoy, J. (2020). Advanced Models for Stylometric Applications. In: Machine Learning Methods for Stylometry. Springer, Cham. https://doi.org/10.1007/978-3-030-53360-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-53359-5

  • Online ISBN: 978-3-030-53360-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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