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    A Deep Context Grammatical Model For Authorship Attribution


    Fuller, Simon and Maguire, Phil and Moser, Philippe (2014) A Deep Context Grammatical Model For Authorship Attribution. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014). European Language Resources Association, pp. 4488-4492. ISBN 9782951740884

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    Abstract

    We define a variable-order Markov model, representing a Probabilistic Context Free Grammar, built from the sentence-level, delexicalized parse of source texts generated by a standard lexicalized parser, which we apply to the authorship attribution task. First, we motivate this model in the context of previous research on syntactic features in the area, outlining some of the general strengths and limitations of the overall approach. Next we describe the procedure for building syntactic models for each author based on training cases. We then outline the attribution process – assigning authorship to the model which yields the highest probability for the given test case. We demonstrate the efficacy for authorship attribution over different Markov orders and compare it against syntactic features trained by a linear kernel SVM. We find that the model performs somewhat less successfully than the SVM over similar features. In the conclusion, we outline how we plan to employ the model for syntactic evaluation of literary texts.

    Item Type: Book Section
    Additional Information: The LREC 2014 Proceedings are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
    Keywords: Authorship Attribution; Syntactic Features; Markov Models;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 6518
    Depositing User: Philippe Moser
    Date Deposited: 03 Nov 2015 16:58
    Publisher: European Language Resources Association
    Refereed: Yes
    URI:

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