Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Author Identification Using Imbalanced and Limited Training Texts
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Author identification: Using text sampling to handle the class imbalance problem
Information Processing and Management: an International Journal
Tensor Space Models for Authorship Identification
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
A survey of modern authorship attribution methods
Journal of the American Society for Information Science and Technology
Authorship attribution and verification with many authors and limited data
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Authorship attribution using probabilistic context-free grammars
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Authorship attribution in the wild
Language Resources and Evaluation
Local histograms of character N-grams for authorship attribution
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
N-Gram feature selection for authorship identification
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
A weighted profile intersection measure for profile-based authorship attribution
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Plagiarism detection using stopword n-grams
Journal of the American Society for Information Science and Technology
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Recent work on Authorship Attribution (AA) proposes the use of meta characteristics to train author models. The meta characteristics are orthogonal sets of similarity relations between the features from the different candidate authors. In that approach, the features are grouped and processed separately according to the type of information they encode, the so called linguistic modalities. For instance, the syntactic, stylistic and semantic features are each considered different modalities as they represent different aspects of the texts. The assumption is that the independent extraction of meta characteristics results in more informative feature vectors, that in turn result in higher accuracies. In this paper we set out to the task of studying the empirical value of this modality specific process. We experimented with different ways of generating the meta characteristics on different data sets with different numbers of authors and genres. Our results show that by extracting the meta characteristics from splitting features by their linguistic dimension we achieve consistent improvement of prediction accuracy.