WordNet: a lexical database for English
Communications of the ACM
Automatic text categorization in terms of genre and author
Computational Linguistics
Language independent authorship attribution using character level language models
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
An Algorithm for Identifying Authors Using Synonyms
ENC '07 Proceedings of the Eighth Mexican International Conference on Current Trends in Computer Science
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Unsupervised decomposition of a document into authorial components
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Use fewer instances of the letter "i": toward writing style anonymization
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
Adversarial stylometry: Circumventing authorship recognition to preserve privacy and anonymity
ACM Transactions on Information and System Security (TISSEC)
Authorship attribution as a case of anomaly detection: A neural network model
International Journal of Hybrid Intelligent Systems
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The writing style of an author is a phenomenon that computer scientists and stylometrists have modeled in the past with some success. However, due to the complexity and variability of writing styles, simple models often break down when faced with real world data. Thus, current trends in stylometry often employ hundreds of features in building classifier systems. In this paper, we present a novel set of synonym-based features for author recognition. We outline a basic model of how synonyms relate to an author's identify and then build an additional two models refined to meet real world needs. Experiments show strong correlation between the presented metric and the writing style of four authors with the second of the three models outperforming the others. As modern stylometric classifier systems demand increasingly larger feature sets, this new set of synonym-based features will serve to fill this ever-increasing need.