Comparative evaluation of term selection functions for authorship attribution

Comparative evaluation of term selection functions for authorship attribution

Different computational models have been proposed to automatically determine the most probable author of a disputed text (authorship attribution). These models can be viewed as special approaches in the text categorization domain. In this perspective, in a first step we need to determine the most effective features (words, punctuation symbols, part-of-speech, bigram of words, etc.) to discrimin...

Quantitative Authorship Attribution: An Evaluation of Techniques

The basic assumption of quantitative authorship attribution is that the author of a text can be selected from a set of possible authors by comparing the values of textual measurements in that text to their corresponding values in each possible author’s writing sample. Over the past three centuries, many types of textual measurements have been proposed, but never before have the majority of thes...

Optimum Parameter Selection for K.L.D. Based Authorship Attribution in Gujarati

We examine several quantitative techniques of authorship attribution that have gained importance over the time and compare them with the current state of the art Z-score based technique. In this paper we show how comparable the existing techniques can be to the Z-score based method, simply by tuning the parameters. We try to find the optimum values for number of terms, smoothing parameter value...

Authorship Attribution

Authorship attribution, the science of inferring characteristics of the author from the characteristics of documents written by that author, is a problem with a long history and a wide range of application. Recent work in “non-traditional” authorship attribution demonstrates the practicality of automatically analyzing documents based on authorial style, but the state of the art is confusing. An...

Authorship Attribution for Electronic Documents