The use of orthogonal similarity relations in the prediction of authorship

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-37256-8_38Recent 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.This research was partially supported by ONR grant N00014-12-1-0217 and by NSF award 1254108. It was also supported in part by the CONACYT grant 134186 and by the European Commission as part of the WIQ-EI project (project no. 269180) within the FP7 People Programme.Sapkota, U.; Solorio, T.; Montes Gómez, M.; Rosso, P. (2013). The use of orthogonal similarity relations in the prediction of authorship. En Computational Linguistics and Intelligent Text Processing. Springer Verlag (Germany). 463-475. https://doi.org/10.1007/978-3-642-37256-8_38S463475Baker, L.D., McCallum, A.: Distributional clustering of words for text classification. 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Redundancy in Systems which Entertain a Model of Themselves: Interaction Information and the Self-organization of Anticipation

Mutual information among three or more dimensions (mu-star = - Q) has been considered as interaction information. However, Krippendorff (2009a, 2009b) has shown that this measure cannot be interpreted as a unique property of the interactions and has proposed an alternative measure of interaction information based on iterative approximation of maximum entropies. Q can then be considered as a measure of the difference between interaction information and redundancy generated in a model entertained by an observer. I argue that this provides us with a measure of the imprint of a second-order observing system -- a model entertained by the system itself -- on the underlying information processing. The second-order system communicates meaning hyper-incursively; an observation instantiates this meaning-processing within the information processing. The net results may add to or reduce the prevailing uncertainty. The model is tested empirically for the case where textual organization can be expected to contain intellectual organization in terms of distributions of title words, author names, and cited references