PV030 Textual Information Systems

Faculty of Informatics
Spring 2006
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Petr Sojka, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. Petr Sojka, Ph.D.
Timetable
Wed 10:00–11:50 D2, Thu 17:00–17:50 B410, Thu 17:00–17:50 B311
  • Timetable of Seminar Groups:
PV030/01: Thu 16:00–16:50 B410, Thu 16:00–16:50 B311, P. Sojka
PV030/03: Thu 18:00–18:50 B410, Thu 18:00–18:50 B311, P. Sojka
Prerequisites
! P030 Textual Information Systems
Students are strongly adviced to bring some basic knowledge of automata theory (IB005) and natural language processing (IB030 or IB047). Some database basics (PB154) will be helpful as well.
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
there are 18 fields of study the course is directly associated with, display
Course objectives
Basic techniques and algorithms used in textual information systems are taught. That means text search algorithms (KMP, AC, BM, RK, ...), data structures used for index storage, query languages, architecture of textual information system that uses natural language processing techniques.
Syllabus
  • Basic notions. TIS - text information system. Classification of information systems.
  • Searching in TIS. Searching and pattern matching classification and data structures.
  • Algorithms of Knuth-Morris-Pratt, Aho-Corasick. Boyer-Moore, Commentz-Walter, Buczilowski.
  • Theory of automata for searching. Classification of searching problems.
  • Indexes. Indexing methods. Data structures for searching and indexing.
  • Google as an examples of search and indexing engine.
  • Signature methods.
  • Query languages and document models: boolean, vector, probabilistic, MMM, Paice.
  • Data compression. Basic notions. Statistic methods.
  • Compression methods based on dictionary. Neural nets for text compression.
  • Syntactic methods. Context modelling.
  • Spell checking. Filtering information channels. Document classification.
Literature
  • Jaroslav Pokorn\'y, V\'aclav Sn\'a\v{s}el, Du\v{s}an H\'usek: Dokumentografick\'e informa\v{c}n\'{\i} syst\'emy, skripta MFF UK Praha, 1998.
  • KORFHAGE, Robert R. Information storage and retrieval. New York: Wiley Computer Publishing, 1997, xiii, 349. ISBN 0471143383. info
  • Information retrieval :data structures & algorithms. Edited by William B. Frakes - Ricardo Baeza-Yates. Upper Saddle River: Prentice Hall, 1992, viii, 504. ISBN 0-13-463837-9. info
  • Finite-state language processing. Edited by Emmanuel Roche - Yves Schabes. Cambridge: Bradford Book, 1997, xv, 464. ISBN 0262181827. info
Assessment methods (in Czech)
Výuka probíhá klasickým způsobem a je zakončena písemným testem (tvoří 70 % hodnocení). Příklady testů z předchozích let jsou vystaveny na webu předmětu. 30 % závěrečného hodnocení tvoří hodnocení písemek zadávaných v průběhu semestru na cvičeních. Na cvičeních dochází k procvičování látky z přednášek, k brainstormingu. V průběhu výuky jsou studenti motivováni dílčími úkoly honorovanými udělením prémiových bodů.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
http://www.fi.muni.cz/~sojka/PV030/
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013.
  • Enrolment Statistics (Spring 2006, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2006/PV030