FI:PA164 Learning and natural language - Course Information

PA164 Machine learning and natural language processing

Extent and Intensity

2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).

Teacher(s)

doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Mgr. Juraj Jurčo (assistant)

Guaranteed by

prof. RNDr. Mojmír Křetínský, CSc.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Computer Science – Faculty of Informatics

Timetable

Wed 10:00–11:50 B410

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Applied Informatics (programme FI, N-AP)
• Information Technology Security (programme FI, N-IN)
• Bioinformatics (programme FI, N-AP)
• Czech Language with Orientation on Computational Linguistics (programme FF, B-FI)
• Information Systems (programme FI, N-IN)
• Informatics (eng.) (programme FI, D-IN4)
• Informatics (programme FI, D-IN4)
• Informatics (programme FI, N-IN)
• Parallel and Distributed Systems (programme FI, N-IN)
• Computer Graphics (programme FI, N-IN)
• Computer Networks and Communication (programme FI, N-IN)
• Computer Systems and Technologies (eng.) (programme FI, D-IN4)
• Computer Systems and Technologies (programme FI, D-IN4)
• Computer Systems (programme FI, N-IN)
• Embedded Systems (eng.) (programme FI, N-IN)
• Embedded Systems (programme FI, N-IN)
• Service Science, Management and Engineering (eng.) (programme FI, N-AP)
• Service Science, Management and Engineering (programme FI, N-AP)
• Social Informatics (programme FI, B-AP)
• Theoretical Informatics (programme FI, N-IN)
• Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
• Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
• Image Processing (programme FI, N-AP)

Course objectives

Students will obtain knowledge about methods and tools for text mining and natural language learning. At the end of the course students should be able to create systems for text analysis by machine learning methods. Students are able to understand, explain and exploit contents of scientific papers from this area.

Syllabus

• Natural language processing(NLP). Corpora. Tools for NLP.
• Inroduction to machine learning
• Disambiguation. Morphological disambiguaiton and word-sense disambiguation
• Shallow parsing and machine learning
• Entity recognition and collocations
• Document categorization
• Information extraction from text
• Text mining
• Web mining
• Applications: text with spatio-temporal information, biomedical and biological texts.

Literature

recommended literature
• MANNING, Christopher D. and Hinrich SCHÜTZE. Foundations of statistical natural language processing. Cambridge: MIT Press, 1999, xxxvii, 68. ISBN 0-262-13360-1. info
• Learning language in logic. Edited by Sašo Džeroski - James Cussens. Berlin: Springer, 2000, x, 299. ISBN 3540411453. info
• LIU, Bing. Web data mining : exploring hyperlinks, contents, and usage data. Berlin: Springer, 2007, xix, 532. ISBN 9783540378815. info

Teaching methods

a lecture combined with demonstrations and a work on a project

Assessment methods

Combination of written and oral examination. A defence of a project is as a part of the examination.

Language of instruction

Czech

Further Comments

Study Materials

Teacher's information

http://www.fi.muni.cz/~popel/lectures/ll/

• Enrolment Statistics (Autumn 2012, recent)
  
• Permalink: https://is.muni.cz/course/fi/autumn2012/PA164