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
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: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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