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COLING
2008

Authorship Attribution and Verification with Many Authors and Limited Data

10 years 1 months ago
Authorship Attribution and Verification with Many Authors and Limited Data
Most studies in statistical or machine learning based authorship attribution focus on two or a few authors. This leads to an overestimation of the importance of the features extracted from the training data and found to be discriminating for these small sets of authors. Most studies also use sizes of training data that are unrealistic for situations in which stylometry is applied (e.g., forensics), and thereby overestimate the accuracy of their approach in these situations. A more realistic interpretation of the task is as an authorship verification problem that we approximate by pooling data from many different authors as negative examples. In this paper, we show, on the basis of a new corpus with 145 authors, what the effect is of many authors on feature selection and learning, and show robustness of a memory-based learning approach in doing authorship attribution and verification with many authors and limited training data when compared to eager learning methods such as SVMs and ma...
Kim Luyckx, Walter Daelemans
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where COLING
Authors Kim Luyckx, Walter Daelemans
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