Natural language processing 1000-2M15PJN
1. Computational Linguistics:
a) introduction, corpora, tokenization (1),
b) morphology, parts of speech (2),
c) syntax (3-5),
d) semantics (6-8);
2. Text classification (9):
a) sentiment analysis,
b) opinion mining;
3. Information Extraction (10);
4. Natural Language Generation (11).
5. Textual Entailment (12);
6. Question Answering (13).
7. Automatic Summarization (14);
8. Machine Translation (15).
Main fields of studies for MISMaP
Type of course
Mode
Prerequisites
Learning outcomes
Knowledge:
1. Knows the basic NLP methodologies
2. Knows the basic issues related to NLP
3. Knows techniques and tools for natural language processing
Skills:
1. Is able to apply NLP techniques in practice
2. Uses the tools for natural language processing
Competences:
1. Is able to search for appropriate knowledge in NLP literature and solve and solve a given NLP task using it.
Assessment criteria
The final grade based on the points from the tasks (programs), written exam and oral exam. The subject of the oral exam is the content of the lecture. Oral exam may be passed by means of attending at the lecture.
Bibliography
Speech and Language Processing (2nd Edition) Daniel Jurafsky , James H. Martin , 2008
Mining Text Data, Charu C. Aggarwal, ChengXiang Zhai, Springer, 2012
Natural Language Processing with Python, Steven Bird, Ewan Klein, Edward Loper, 2009
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: