Abstract
We define a variable-order Markov model, representing a Probabilistic Context Free Grammar, built from the sentence-level, de-lexicalized 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.- Anthology ID:
- L14-1461
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 4488–4492
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/568_Paper.pdf
- DOI:
- Cite (ACL):
- Simon Fuller, Phil Maguire, and Philippe Moser. 2014. A Deep Context Grammatical Model For Authorship Attribution. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 4488–4492, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- A Deep Context Grammatical Model For Authorship Attribution (Fuller et al., LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/568_Paper.pdf