= Stylometry =

[[https://is.muni.cz/auth/predmet/fi/ia161|IA161]] [[en/AdvancedNlpCourse|Advanced NLP Course]], Course Guarantee: Aleš Horák

Prepared by: Honza Rygl

== 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:
 1. specific style markers are extracted,
 2. a classification procedure is applied to extracted markers


=== References ===

 1. 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]]
 2. Kestemont, M. (2014), Function Words in Authorship Attribution From Black Magic to Theory? Proceedings of the 3rd Workshop on Computational Linguistics for Literature, EACL 2014, 59–66 [[http://aclweb.org/anthology/W14-0908 | pdf]]
 1. Walter, D.  Explanation in Computational Stylometry

== Practical Session ==

Student will get to know a *Style & Identity Recognition* tool. They will test this tool on prepared data.
Their goal will be to implement a small function to extract style markers from a text.