Infopaket

Stylometry and Text Attribution

ДВС.1. 02.04

Вибіркова дисципліна для ОП

First

2022/2023

8 Semester

2

PLO 7. To understand major problems of philology and approaches to their solution with the use of relevant methods, in particular, innovative interdisciplinary approaches of applied (computational) linguistics and information technologies; to explain their interrelation in the integrated system of interdisciplinary knowledge. PLO 11. To know the principles, technologies, and methods for creating oral and written texts of different genres and styles in official and foreign (English) languages and be able to use them in professional activity. PLO 12. To analyze language units, phenomena, and processes by methods of structural, mathematical, and computational linguistics; to represent the processes of analysis and synthesis of linguistic objects in an algorithmic way. PLO 16. To know and understand the major notions, theories, and conceptions of structural, mathematical, and computational linguistics, and to be able to use them in professional activity. PLO 17.

Full-time form

1. Successful completion of courses: "Quantitative Linguistics", "Traditional and Computer Lexicography", "Automatic Morphological Analysis", and "Automatic Syntactic Analysis" 2. Program-level knowledge of methods of structural linguistics, linguistic statistics, computer linguistics, and ability to model linguistic phenomena; 3. Having elementary skills in abstracting a scientific text.

The goal of the discipline is an in-depth introduction to the semantic and formal - quantitative and classification - methods of text analysis, which are part of linguistic self-study examination. The educational discipline consists of two content parts: Part 1. "Content module 1 "Theoretical principles of authorization" (1 credit); Part 2. "Statistical methods of analyzing the style of anonymous texts" (1 credit). Part 1 is aimed at getting acquainted with the qualitative methods of self-research examination; Part 2, "Statistical methods of analyzing the style of anonymous texts" is aimed at the study of quantitative methods of autobiographical examination.

Interpreting Burrows’s Delta: Geometric and Probabilistic Founda-tions // Literary and Linguistic Computing. Vol. 23. 2007. P. 131–147.Burrows 2002: Burrows J. “Delta”: a Measure of Stylistic Difference and a Guide to Likely Author-ship // Literary and Linguistic Computing. Vol. 17(3). 2002. P. 267–287.Burrows et al. 2014: Burrows S., Uitdenbogerd A. L., Turpin A. Comparing techniques for author-ship attribution of source code // Software: Practice and Experience. Vol 44. 2014. P. 1–32.Eder 2013: Eder M. Mind Your Corpus: Systematic Errors in Authorship Attribution // Literaryand Linguistic Computing. Vol. 28(4). 2013. Р. 603–614.Eder 2015: Eder M. Does size matter? Authorship attribution, small samples, big problem // DigitalScholarship in the Humanities. Vol. 30. No. 2. 2015.Eder 2017: Eder M.Short samples in authorship attribution: A new approach // Digital Humani-ties 2017: Conference Abstracts. Montreal: McGill University. P. 221–224.Evert etal.2017:

Lectures, seminars, and laboratory classes, independent work. Types of work: Answer in a seminar/laboratory session; Written independent test; Creative project (laboratory work).

During the semester, after lectures on relevant topics, seminars, and laboratory classes are held, at which evaluation is carried out according to the types of work. The final assessment is conducted in the form of an exam: • the maximum number of points on the exam is 40 points, the minimum number of points (positive score) that are added to semester grades is 24 points (60% of the maximum number of points assigned to the exam); • the exam is conducted in the form of a written work, the exam ticket consists of two open theoretical questions, each of which is evaluated with a maximum of 20 points. The semester final score is formed by the points received by the student in the process of performing the declared types and forms of education, and the points received on the exam, the maximum distribution is carried out according to the following algorithm: 60 points (60%) - semester control and 40 points (40%) - exam).

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