13M111OPJ - Natural language processing

Course specification
Course title		Natural language processing
Acronym		13M111OPJ
Study programme		Electrical Engineering and Computing
Module		Software Engineering
Type of study		master academic studies
Lecturer (for classes)		professor PhD Boško Nikolić PhD Vuk Batanović
Lecturer/Associate (for practice)		professor PhD Boško Nikolić PhD Vuk Batanović
Lecturer/Associate (for OTC)		professor PhD Boško Nikolić PhD Vuk Batanović
ESPB		6.0	Status	elective
Condition		None
The goal		Introduce students to the basic concepts and techniques of statistical Natural language processing (NLP). A comparative analysis of used machine learning methods. Presentation of the main morphological, syntactic and semantic problems in the computer NLP. During the course, students will study the implementation of the popular models of these types of applications.
The outcome		Students will be able to recognize a problem that belongs to the field Natural language processing (NLP), and based on their knowledge apply the most appropriate and most effective method for its solution.
Contents
URL to the subject page		http://rti.etf.bg.edu.rs/rti/ms1opj/
Contents of lectures		Machine learning in natural language processing. Generative and discriminant models. Sequence models. Overview of the morphological, syntax, and semantic problems. Linguistic models. Stemming and lemmatization. Marking word types. Parsing. Text classification based on thematic and sentiment. Lexical semantics. Distributional semantics. Semantic similarity. Recognition of entities.
Contents of exercises		Laboratory demonstration exercises. Common conception and elaboration of topic and project content; referral to relevant concepts, approaches, tools and literature; monitoring and discussing the solutions, results and possible improvements during project work and its documenting.
Literature
Dan Jurafsky, James H. Martin, "Speech and Language Processing", Prentice Hall, 2008. (Original title) Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, "An Introduction to Information Retrieval", Cambridge University Press, 2008. (Original title)
Number of hours per week during the semester/trimester/year
Lectures	Exercises	OTC	Study and Research	Other classes
2	2	1
Methods of teaching		Lectures, presentations, practical exercises, individual work on projects,
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures			Test paper	30
Practical lessons			Oral examination
Projects		70
Colloquia
Seminars