Magistracy
Speciality Code:
7M06101
Speciality Name:
Computational Linguistics
Faculty:
Information technology
Qualification:
- Scientific and pedagogical direction - Master of Engineering Sciences
- Model of graduating student
- Mandatory disciplines
- Elective disciplines
- Professional
Upon completion of this educational program, it is expected that undergraduates will be able to:
1.discuss methodologies and technological advances in computational linguistics and natural language processing;
2.choose methodologies and technologies for solving problems of natural language processing;
3. use automated text translation;
4. analyze the grammatical system of Kazakh and other languages;
5. compare and contrast languages in terms of systematic differences in phonetics, phonology, morphology, syntax and semantics;
6. use the OEW tools for analyzing large sets of documents, defining topics and summarizing;
7. develop language resources and tools of the Kazakh language;
8. develop the resources and tools of OYA (natural language processing);
9. create parallel and comparable corps between Kazakh and foreign languages;
10. to make his own original contribution to the development of the field of computational linguistics: prepare publications, scientific and technical reports, reviews based on the results of completed studies;
11. conduct scientific and pedagogical activity, participate in the development of educational and methodological materials for teaching disciplines in the direction of the educational program "Computational Linguistics";
12. formulate solutions to problems based on research in the field of information systems by integrating knowledge from new or interdisciplinary areas and taking into account social, ethical, linguistic and scientific considerations.
1.discuss methodologies and technological advances in computational linguistics and natural language processing;
2.choose methodologies and technologies for solving problems of natural language processing;
3. use automated text translation;
4. analyze the grammatical system of Kazakh and other languages;
5. compare and contrast languages in terms of systematic differences in phonetics, phonology, morphology, syntax and semantics;
6. use the OEW tools for analyzing large sets of documents, defining topics and summarizing;
7. develop language resources and tools of the Kazakh language;
8. develop the resources and tools of OYA (natural language processing);
9. create parallel and comparable corps between Kazakh and foreign languages;
10. to make his own original contribution to the development of the field of computational linguistics: prepare publications, scientific and technical reports, reviews based on the results of completed studies;
11. conduct scientific and pedagogical activity, participate in the development of educational and methodological materials for teaching disciplines in the direction of the educational program "Computational Linguistics";
12. formulate solutions to problems based on research in the field of information systems by integrating knowledge from new or interdisciplinary areas and taking into account social, ethical, linguistic and scientific considerations.
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Natural Language Understanding
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Discipline is aimed at students mastering the basics of automatic text processing, written in natural language. This implies not only the ability to use ready-made applications for linguistic analysis, also an understanding of principles of their work, as well as familiarity with basic mathematical models that underlie modern computational linguistics.
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Machine Translation Technologies
- Number of credits: 9
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose: to develop ability of master students to work with machine translation systems to develop effective machine translation algorithms. As a result, master student must be able to: apply results obtained in development of automatic systems to extract new knowledge of natural language; evaluate hybrid and statistical approaches.
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Introduction to programming for natural language processing (Python, R, Prolog)
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability of master students to create programs for the NLP in order to increase the effectiveness of their subsequent use. The main focus of the discipline is study of Python programming language, which is one of the standard tools in NLP.
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Foreign Language (professional)
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose is to acquire and improve competencies by international standards of foreign language education and to communicate in an intercultural, professional, and scientific environment. A master's student must integrate new information, understand the organization of languages, interact in society, and defend his point of view.
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History and Philosophy of Science
- Number of credits: 3
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The course forms knowledge about the history and theory of science; on the laws of the development of science and the structure of scientific knowledge; about science as a profession and social institution; оn the methods of conducting scientific research; the role of science in the development of society.
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Organization and Planning of Scientific Research (in English)
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to train masters in conducting scientific research, carrying out scientific and methodological work, socializing young students and their participation in the corporate governance system of Organization of higher and postgraduate education (OHPE). Undergraduates learn to interact with OHPE stakeholders, participate in research projects.
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Pedagogy of Higher education
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose is the formation of the ability of pedagogical activity through the knowledge of higher education didactics, theories of upbringing and education management, analysis, and self-assessment of teaching activities. The course covers the educational activity design of specialists, Bologna process implementation, acquiring a lecturer, and curatorial skills by TLA-strategies.
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Psychology of Management
- Number of credits: 3
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The course reveals the subject, the basic principles of management psychology, personality in managerial interactions, personal behavior management, psychology of managing group phenomena and processes, psychological characteristics of the leader's personality, individual management style, psychology of influence in management activities, conflict management.
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Ontologies and semantic technologies
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the abilities of master students to design and implement software solutions in the field of semantic technologies. The studying aspects: goals and objectives of the use of methods and means of knowledge representation, the main types of information and knowledge representation, etc.
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Programming technologies for NLP
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to learn the Python language and learn how to apply it to solve problems of data analysis and machine learning in NLP. Basic constructs and idioms of the Python language;, use and apply in-depth knowledge of the field in NLP
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Machine learning methods in natural language processing
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The maintenance of the course includes the identification of the types of problems solved by methods of machine learning, and the choice of suitable methods; studying methods of classification (metric, logical, linear), prediction methods, clustering methods, ensemble methods of machine learning algorithms; the construction of various models of machine learning and consideration of ways of quality assessment
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Formal Grammars
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability of graduate students to work with formal grammars of natural languages in order to improve models of natural language processing. demonstrate an understanding of the formal apparatus for describing algorithmic languages: regular expression systems, context-free grammars, state machines without memory and with stack memory;
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Language analysis
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The concept of a linguistic sign and system. Language as a means of presenting information. Levels of language analysis - Lexical, Morphological Semantic analysis and synthesis. Denotative aspects of the word, phrase. Suggestion and methods for automatic detection of syntactic structure. Problems of generating sentences and text.
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Models and methods of neural networks in NLP
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The course introduces the problems of natural language processing and machine learning, forms knowledge about simple and advanced vector representations of words, architecture and models of neural networks. studying the issues of network design, training and selection of parameters; study of recurrent and recursive neural networks of NLP.
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Deep leaning
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal of the discipline is to develop the ability to use deep learning models and methods for the implementation, as well as to improve understanding of current research in the field of word processing. Apply tools and design and implement deep learning systems to solve practical problems.
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Computational morphology
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability of undergraduates to work with formal models of automatic text processing in natural language for machine learning tasks. apply modern programming languages and data manipulation languages, operating systems, software packages, etc. in research and applied activities;
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Machine learning in natural language processing
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose: formation of deep theoretical knowledge in field of machine learning, including discriminant, cluster and regression analysis, mastering skills of practical solving problems of data mining; also formation of ability to use standard software packages for machine learning and the implementation of linear and non-linear models for data classification.
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Methods for information retrieval and extraction
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of discipline: formation of necessary theoretical base and practical skills for master students, which will allow to comprehensively and systematically understand modern problems of applied mathematics and computer science, problems of information processing and analysis, and data extraction methods.
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Speech Processing
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to form the ability to use hidden Markov models for time-varying signals modeling, principles of language modeling and strategies for reducing noise signals, deep learning methods for creating modern systems for spoken language processing.
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Ontologies and semantic technologies
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the abilities of master students to design and implement software solutions in the field of semantic technologies. The studying aspects: goals and objectives of the use of methods and means of knowledge representation, the main types of information and knowledge representation, etc.
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Statistical methods for natural language processing
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose: to learn about various statistical methods commonly used in NLP and to explore the ways in which those methods have been applied to various linguistic problems. This course covers a broad range of topics in NLP, text classification and disambiguation.
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Sentiment analysis technology
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal of the discipline is the formation of theoretical knowledge about the methods and tools used for sentiment analysis, the areas of application of sentiment analysis, as well as practical skills in data collection, text preproduction, mood determination, mood classification and visualization of results.
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Formal grammars
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of discipline is to develop ability of master students to work with formal grammars of natural languages for improving models of processing natural languages; analyze algorithms of lexical, syntactic and semantic analysis, implemented by appropriate finite automata; evaluate methods of generating object code for a specific target machine.
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Language Resources
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is the formation of the ability of master student to work with language resources and databases for computational linguistics for their use in the processing of language texts; use the principles of maintaining integrity and maintaining security in databases; create queries to databases.
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Data mining
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to use theoretical and practical knowledge when working with data of various volumes and complexity. The discipline is aimed at studying the methods of data collection, processing and analysis to further identify patterns
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Information technology for NLP
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Study of modern information technologies and tools for NLP. Levels of language analysis. The lexical level of the language in automatic processing. Denotative aspects of the word, phrase. Suggestion and methods for automatic detection of syntactic structure. Ways of encoding the semantic content of the text and its automatic processing. Natural language interfaces
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Natural language understanding
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Discipline is aimed at students mastering the basics of automatic text processing, written in natural language. This implies not only the ability to use ready-made applications for linguistic analysis, also an understanding of principles of their work, as well as familiarity with basic mathematical models.
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Pedagogical
- Type of control: Защита практики
- Description: Aim оf discipline: formation of the ability to carry out educational activities in universities, to design the educational process and conduct certain types of training sessions using innovative educational technologies.
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Research
- Type of control: Защита практики
- Description: The purpose of the practice: gaining experience in the study of an actual scientific problem, expand the professional knowledge gained in the learning process, and developing practical skills for conducting independent scientific work. The practice is aimed at developing the skills of research, analysis and application of economic knowledge.