Authorship attribution with author-aware topic models

Authors:
Yanir Seroussi;Fabian Bohnert;Ingrid Zukerman
Affiliations:
Monash University, Clayton, Victoria, Australia;Monash University, Clayton, Victoria, Australia;Monash University, Clayton, Victoria, Australia
Venue:
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Year:
2012

Citing 10
Cited 0

Latent dirichlet allocation

The Journal of Machine Learning Research
The author-topic model for authors and documents

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
LIBLINEAR: A Library for Large Linear Classification

The Journal of Machine Learning Research
Computational methods in authorship attribution

Journal of the American Society for Information Science and Technology
A survey of modern authorship attribution methods

Journal of the American Society for Information Science and Technology
Learning author-topic models from text corpora

ACM Transactions on Information Systems (TOIS)
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora

EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Authorship attribution in the wild

Language Resources and Evaluation
Authorship attribution with latent Dirichlet allocation

CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Probabilistic topic models

Communications of the ACM

Quantified Score

Hi-index	0.00

Visualization

Abstract

Authorship attribution deals with identifying the authors of anonymous texts. Building on our earlier finding that the Latent Dirichlet Allocation (LDA) topic model can be used to improve authorship attribution accuracy, we show that employing a previously-suggested Author-Topic (AT) model outperforms LDA when applied to scenarios with many authors. In addition, we define a model that combines LDA and AT by representing authors and documents over two disjoint topic sets, and show that our model outperforms LDA, AT and support vector machines on datasets with many authors.