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Stylometry

-                      v6
+                      v7
 == State of the Art ==
+The analysis of author's characteristic
+writing style and vocabulary has been used to uncover author's traits such as authorship, age, or gender
+documents by both manual linguistic approaches and automatic algorithmic methods.
+The most common approach to stylometry problems
+is to combine stylistic analysis with machine learning techniques:
+) specific style markers are extracted,
+) a classification procedure is applied to extracted markers
 === References ===
-Approx 3 current papers (preferably from best NLP conferences/journals, eg. [[https://www.aclweb.org/anthology/|ACL Anthology]]) that will be used as a source for the one-hour lecture:
 . Stamatatos, E. (2009), A Survey of Modern Authorship Attribution Methods (2009), Journal of the American Society for Information Science and Technology, 60(3), 538-556. [[http://www.clips.ua.ac.be/~walter/educational/material/Stamatatos_survey2009.pdf | pdf]]
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 == Practical Session ==
+Student will have to
 Concrete description of work assignment for students for the second one-hour part of the lecture. The work will consist of tasks connected with practical implementations of algorithms connected with the current topic (probably not the state-of-the-art algorithms mentioned in the first part) and with real data. Students can test the algorithms, evaluate them and possibly try some short adaptations for various subtasks.