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Computer Linguistics
Major: System Analysis
Code of subject: 6.124.03.E.069
Credits: 6.00
Department: Information Systems and Networks
Lecturer: PhD, associate professor Vysotska Victoria Anatoliivna
Semester: 6 семестр
Mode of study: денна
Завдання: Studying the academic discipline involves the formation of competencies in students:
professional competencies:
FKS3.1. The ability to effectively conduct system analysis, conduct research, extract and analyze data from various information resources based on mathematical models and data science methods for decision-making support processes.
Learning outcomes: As a result of studying the academic discipline, the student should be able to:
1). The student should know and understand the basic definitions, statements and theorems, methods of proving statements, areas and methods of applying the acquired linguistic knowledge of natural language processing from the following sections:
• natural language processing;
• text normalization;
• distance editing;
• language modeling using N-grams;
• statistical processing of natural languages;
• spam filtering;
• search engine development;
• text classification and sentimentalization;
• spelling correction and noisy channel;
• sentiment analysis;
• sarcasm identification.
2). A trained specialist must be able to apply the acquired knowledge to
• formulate and solve problems in the field of computer science for processing natural language,
• construct algorithms for solving applied linguistic problems,
• study new information technologies for processing natural languages,
• construct a mathematical description of applied linguistic problems,
• analyze the results of solving problems for processing natural languages.
As a result of studying the academic discipline, the student must be able to demonstrate the following learning outcomes:
PRS3.6. Ability to have sufficient knowledge of mathematical models and methods for building linguistic support for computer data processing systems to perform practical tasks.
Required prior and related subjects: Machine Learning
Database and Knowledge Organization
Object-Oriented Programming
Deep Learning
Data Analytics Innovations
Big Data Technologies
Summary of the subject: As a result of studying the course "Computational Linguistics", students should know the methods of computational linguistics and be able to use them when solving scientific and scientific and technical problems in such fields as computer science and information technology.
Опис: 1. Introduction to Computational Linguistics and NLP (Natural Language Processing). Types of NLP Applications
2. Regular Expressions, Text Normalization, Distance Editing. Language Modeling with N-grams. Fundamentals of Statistical Natural Language Processing. Spam Filtering. Search Engines
3. Naive Bayesian Classification and Sentimentization. Statistical Methods. Logistic Regression. Spelling Correction and Noisy Channel. Sentiment Analysis. Sarcasm Detection.
4. Vector Semantics and Embedding. Neural Networks and Neural Language Models. Hidden Markov Models. Machine Learning. Essay Scoring. Identifying Good/Bad Characters
5. Part-of-Speech Tagging. Sequential Processing with Recurrent Networks. Encoder-Decoder Models, Attention, and Contextual Embedding. Machine Translation. Neural Machine Translation. Error Correction. Pattern matching
6. Methodology and parsing of immediate components. Statistical parsing of immediate components and dependencies. Stemming. Martin Porter's algorithm. Speech-to-text, text-to-speech
7. Logical representation of sentence meaning. Computational semantics and semantic analysis. Information extraction. Web Mining, Text Mining, Data Mining
8. Word meaning and WordNet. Semantic role marking and argument structure. Lexicons for sentiment, affect and connotation
9. Co-referentiality resolution. Discourse coherence. Text generalization. Summarizing, summarizing and digest formation
10. Question response formation. Dialogue of dialogue systems and chatbots. Phonetics. Speech processing. Conversational agents. Language learning systems. Story ending generation
Assessment methods and criteria: Knowledge diagnostics is carried out by evaluating the completed laboratory work and examination control (written and oral components) in the form of test questions.
Критерії оцінювання результатів навчання: • Current control (45%): laboratory reports, program codes in R or Python, teamwork, oral presentation of developed programs in the team and answers to questions from the teacher and other teams.
• Final control (55%, exam): written and oral form.
Порядок та критерії виставляння балів та оцінок: 100–88 points – (“excellent”) is awarded for a high level of knowledge (some inaccuracies are allowed) of the educational material of the component contained in the main and additional recommended literary sources, the ability to analyze the phenomena being studied in their interrelationship and development, clearly, succinctly, logically, consistently answer the questions, the ability to apply theoretical provisions when solving practical problems; 87–71 points – (“good”) is awarded for a generally correct understanding of the educational material of the component, including calculations, reasoned answers to the questions posed, which, however, contain certain (insignificant) shortcomings, for the ability to apply theoretical provisions when solving practical tasks; 70 – 50 points – (“satisfactory”) awarded for weak knowledge of the component’s educational material, inaccurate or poorly reasoned answers, with a violation of the sequence of presentation, for weak application of theoretical provisions when solving practical problems; 49-26 points - ("not certified" with the possibility of retaking the semester control) is awarded for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to apply theoretical provisions when solving practical problems; 25-00 points - ("unsatisfactory" with mandatory re-study) is awarded for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to navigate when solving practical problems, ignorance of the main fundamental provisions.
Recommended books: 1. Pasichnyk V.V. Mathematical linguistics. Book 1. Quantitative linguistics / V.A. Vysotska, V.V. Pasichnyk, Yu.M. Shcherbyna, T.V. Shestakevych // Textbook with the seal of the Ministry of Education and Science, Youth and Sports of Ukraine. – Lviv: Publishing House “Novyi Svit-2000”, 2012. – 359 p.
2. Pasichnyk V.V. Mathematical linguistics. Book 2. Combinatorial linguistics: textbook / V.V. Pasichnyk, Yu.M. Shcherbyna, V.A. Vysotska, T.V. Shestakevych. – Lviv: Publishing House of Lviv Polytechnic, 2019. – 250 p.
3. Victoria Vysotska. Computer linguistics for online marketing in information technology : Monograph. – Saarbrucken, Germany: LAP LAMBERT Academic Publishing, 2018. – 396 p. – ISBN-13: 978-613-9-84601-6, ISBN-10: 6139846013, EAN: 9786139846016. – Book language: English. – https://www.lap-publishing.com/catalog/details/store/gb/book/978-613-9-84601-6/computer-linguistics-for-online-marketing-in-information-technology?search=vysotska. – Published on: 2018-05-30
4. Bisikalo O.V. Keyword detection based on the content monitoring method of Ukrainian-language texts / O.V. Bisikalo, V.A. Vysotska // Scientific journal "Radioelectronics. Informatics. Management." – No. 1(36). – Zaporizhzhia: ZNTU. – 2016/1. – P. 74-83. – ISSN 1607-3274 (print), ISSN 2313-688X (on-line). – http://ric.zntu.edu.ua/.
5. Lytvyn V.V. Methods and means of processing information resources based on ontologies: monograph / V.V. Lytvyn, V.A. Vysotska, D.G. Dosyn. – Lviv: LA “Pyramid”, 2016. – 404 p.
6. Bisikalo O.V. Method of linguistic analysis of Ukrainian-language commercial content / O.V. Bisikalo, V.A. Vysotska // Information systems and networks. Bulletin of the National University “Lviv Polytechnic”, No. 854.- Lviv 2016 – Pages. 185-204.
7. Vysotska V. A. Methods and means of functioning of decision support systems based on ontologies: monograph / V.A. Vysotska, D.G. Dosyn, K.I. Mykich, I.I. Zavushchak, Z.L. Rybchak. – Lviv: Publishing house “Novyi svit – 2000”, 2019. – 334 p.
8. Victoria Vysotska. Internet systems design and development based on Web Mining and NLP : Monograph. – Saarbrucken, Germany: LAP LAMBERT Academic Publishing, 2018. – 316 p. – ISBN-13: 978-3-659-96245-5, ISBN-10: 3659962457, EAN: 9783659962455. – Book language: English. – https://www.lap-publishing.com/catalog/details/store/ru/book/978-3-659-96245-5/internet-systems-design-and-development-based-on-web-mining-and-nlp?locale=gb. – Published on: 2018-03-30.
9. Methods based on ontologies for information resources processing: Monograph / [Vasyl Lytvyn, Victoria Vysotska, Lyubomyr Chyrun, Dmytro Dosyn] // LAP Lambert Academic Publishing. Saarbrucken, Germany. - ISBN-13: 978-3-659-89905-8, ISBN-10: 3659899054, EAN: 9783659899058. - 2016. - 324 p. - Access mode: https://www.lap-publishing.com/catalog/details/store/gb/book/978-3-659-89905-8/methods-based-on-ontologies-for-information-resources-processing?locale=gb.
10. Lytvyn V.V. Data and knowledge analysis: textbook / V.V. Lytvyn, V.V. Pasichnyk, Yu.V. Nikolsky. - Lviv: "Magnolia 2006", 2013. - 276 p.
11. Lytvyn V.V. Deep learning: textbook / V.V. Lytvyn, R.M. Peleshchak, V.A. Vysotska. - Lviv: Lviv Polytechnic Publishing House, 2021. - 264 p.
12. Kaminsky R.M. Methods and systems of artificial intelligence: textbook / R.M. Kaminsky, N. B. Shakhovska, V. M. Khavalko, A. M. Khudiy. – Lviv: Taras Soroka Publishing House, 2021. – 218 p.
13. Andrunyk V. A. Numerical methods in computer sciences. Volume 1: textbook / V. A. Andrunyk, V. A. Vysotska, V. V. Pasichnyk, L. B. Chyrun, L. V. Chyrun. – Lviv: Novyj Svit – 2000, 2017. – 470 p.
14. Andrunyk V. A. Numerical methods in computer sciences. Volume 2: textbook / V. A. Andrunyk, V. A. Vysotska, V. V. Pasichnyk, L. B. Chyrun, L. V. Chyrun. – Lviv: Novyj Svit – 2000, 2018. – 536 p.
15. Vysotska V.A., Oborska O.V. Python: algorithmization and programming: textbook – Lviv: Publishing house “Novyi Svit – 2000”, 2020. – 516 p.
16. Ryshkovets Y.V., Vysotska V.A. Algorithmization and programming. Part 1: Textbook. – Lviv: “Novyi Svit – 200”, 2018. – 337 p.
17. Ryshkovets Y.V., Vysotska V.A. Algorithmization and programming. Part 2: Textbook. – Lviv: “Novyi Svit – 2000”, 2018. – 316 p.
18. Vysotska V.A., Lytvyn V.V., Lozynska O.V., Discrete Mathematics: Workshop (Collection of Problems in Discrete Mathematics: Textbook. – Lviv: Novy Svit – 2000, 2019. – 575 pp.
19. Berko A.Yu. Systems of Electronic Content Commerce. Monograph / A.Yu. Berko, V.A. Vysotska, V.V. Pasichnyk // Publishing House of the National University “Lviv Polytechnic”. – Lviv 2009. – 612 pp.
20. Vysotska V.A. Content Monitoring of Text Information of Web Resources / V.A. Vysotska, L.B. Chirun, L.V. Chirun // International Scientific Conference “Intelligent Decision-Making Systems and Problems of Computational Intelligence (ISDMIT’2015)”, Zalizny Port, Ukraine. – May 25-28, 2015. – Pp.36-38.
21. Vysotska V.A. Technologies of electronic commerce and Internet marketing: monograph. – Saarbrucken, Germany: LAP LAMBERT Academic Publishing, 2018. – 288 p. – ISBN-13: 978-613-5-94542-3, ISBN-10: 6135945424, EAN: 9786135945423. – https://www.lap-publishing.com/catalog/details/store/ru/book/978-613-5-94542-3/Технологии-електронной-комерции-та-Интернет-маркетингу?search=978-613-5-94542-3
22. Victoria Vysotska, Vasyl Lytvyn. Web resources processing based on ontologies: Monograph. – Saarbrucken, Germany: LAP LAMBERT Academic Publishing, 2018. – 232 p.
23. Victoria Vysotska, Natalya Shakhovska. Information technologies of gamification for training and recruitment : Monograph. – Saarbrucken, Germany: LAP LAMBERT Academic Publishing, 2018. – 248 p.
24. Lytvyn Vasyl. Big Data analytics ontology / Vasyl Lytvyn, Victoria Vysotska, Oleh Veres, Oksana Brodyak, Oksana Oryshchyn // Technology audit and production reserves. Information and control systems. – Vol. 1, No. 2(39). – 2018. – P. 16-27. – http://journals.uran.ua/tarp/article/view/123612/118528
25. Burov E.V. Conceptual modeling of intelligent software systems: monograph / E.V. Burov.– Lviv: Lviv Polytechnic Publishing House, 2012.– 432 p.
26. Berko A.Yu. Database and Knowledge Systems. Book 1. Database Organization: Textbook / Berko A.Yu., Veres O.M., Pasichnyk V.V.– Lviv: “Magnolia 2006”, 2011.– 456 p.
27. Pasichnyk V.V. WEB Technologies: Textbook / Pasichnyk V.V., Pasichnyk O.V., Ugrin D.I. - Lviv: “Magnolia 2006”, 2013.- Part I.– 336 p.
28. Veres O.M. Database and Knowledge Systems. Book 2. Database and Knowledge Management Systems: Textbook [for students of higher education. zakl.] / Berko A.Yu., Veres O.M., Pasichnyk V.V. – Lviv: “Magnolia 2006”, 2013. – 584 p. — (Series “Computer”).
29. Veres O. M. Decision-making support technologies: tutorial / O.M. Veres; under the general scientific editorship of V.V. Pasichnyk.– 2nd ed. – Lviv: Lviv Polytechnic Publishing House, 2013. – 252 p. — (Series “Consolidated Information”, issue 3).
30. Machine learning: tutorial / T.M. Basyuk, V.V. Lytvyn, L.M. Zakharia, N.E. Kunanets. – Lviv: “New World-2000”, 2019. – 315 p.
31. Basyuk T. M. Ontology description languages: a textbook / T. M. Basyuk, V. V. Lytvyn. – Lviv: Publishing House “Lviv Polytechnic”, 2020. – 276 p.
32. Dosyn D. G. Models and methods for determining the usefulness of ontological knowledge: a collective monograph / D. G. Dosyn, V. V. Lytvyn. – Lviv: Novy Svit – 2000, 2021. – 251 p.
33. Veres O.M. Database and knowledge systems. Book 1. Organization of databases and knowledge: a textbook [for students of higher education] / Berko A. Yu., Veres O. M., Pasichnyk V.V. – Lviv: “Magnolia 2006”, 2022. – 440 p. — (Series “Computer”).
34. Veres O.M. Database and knowledge systems. Book 2. Database and knowledge management systems: textbook / Berko A.Yu., Veres O.M., Pasichnyk V.V. – Lviv: “Magnolia 2006”, 2022. – 470 p.
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