Infopaket

Text-Mining

ДВС.1.02

Вибіркова дисципліна для ОП

Second

2021/2022

3 Semester

3

PLO 3. To implement modern methods and techniques, namely informational, in order to perform a successful and effective professional activity and guarantee the quality of research in an applied linguistics field. PLO 19. To apply professional techniques and technologies of web page creation and web design, search engine optimization, copywriting, rewriting, and text compression. PLO 21.1. To apply professionally the knowledge of basic methods and principles of data extraction (Data Mining), an intellectual analysis of text data (Text Mining), organization and types of artificial neural networks, and types of machine learning tasks in the domain of Automatic natural language processing. PLO 22.1. To know and apply systematically the methods of analysis and applied (linguistic) industry modeling, determine the information needs and data collection to design software. PLO 27.1 See the full list of learning outcomes of the educational program in the "Program Profile" section.

Full-time form

To have basic skills in any tool programming environment (Python) and developing programs in high-level languages ​​to complete the given task.

The discipline of the optional block "Automatic Natural Language Processing (NAPL)". The goal of the discipline is the formation of the professional competencies necessary for modeling and structuring the information content of text sources, which has become especially relevant for the current stage of development in the fields of business, politics, social processes, and general humanitarian research. The course involves studying the theoretical foundations of automatic information search based on the analysis of textual data; development of practical skills for automation of intellectual processing of text sources using methods of machine learning and natural language processing (Natural Language Processing); formation of social media analysis skills (Facebook, Twitter, Google, etc.) using the Python programming language.

1. Hadelin de Ponteves. AI Crash Course: A fun and hands-on introduction to machine learning, reinforcement learning, deep learning, and artificial intelligence with Python. Packt Publishing; 2019. 534 p. 2. Daniel Vaughan. Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise. Publisher: ‎ O'Reilly; 2020. 380 p. 3. Laurence Moroney. AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence. Publisher: ‎ O'Reilly, 2020. 394 p. 4. Artificial Intelligence and Intellectual Property. Edited by Jyh-An Lee, Oxford University Press. 2021. 441 p. 6. Deep Learning for Natural Language Processing © Copyright 2017 Jason Brownlee. All Rights Reserved. 414 p.

Lectures, practical classes, independent work.

Student evaluation forms: - semester assessment: 1. Written tests (in practical classes) 4 tests are evaluated for 15 points, a total of 60 points - the maximum number of points for a positive assessment. 2. Modular test: Two tests are evaluated: MCW1 – 20 points, MCW2 – 20 points; a total of 40 points is the maximum number of points for a positive assessment. Final assessment in the form of credit. The credit is given based on the results of the student's work throughout the entire semester and does not include additional assessment measures. Students who scored the minimum positive number of points - 60 - receive - "Passed". Students who did not score the minimum positive number of points - 60 - receive - a "Fail".

Ukrainian, English