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Course, academic year 2019/2020
  
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Information retrieval - P500003
Title: Information retrieval
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2019 to 2019
Semester: summer
Points: summer s.:0
E-Credits: summer s.:0
Examination process: summer s.:
Hours per week, examination: summer s.:3/0, other [HT]
Capacity: unlimited / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Level:  
Note: can be fulfilled in the future
Guarantor: Kroha Petr prof. Dr. Ing. CSc.
Is interchangeable with: AP500003
Examination dates   Schedule   
Annotation -
A number of electronic documents grows much faster than a human is able to deal with. The information retrieval methods help to identify documents likely containing a given information. The selection of documents is based on keywords, that are assigned to characterize document content and used to specify the aims of user search. To achieve this aim, information retrieval utilizes the methods of linear algebra that work with the vector model, statistical and probability methods, methods of computational linguistics or classification and clustering methods of artificial intelligence.
Last update: Svozil Daniel (25.05.2018)
Course completion requirements -

oral exam

Last update: Svozil Daniel (23.05.2018)
Literature -

R: Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Second edition, Addison-Wesley, 2011.

R: Weiss, S.M. et all: Text Mining? Predictive Methods for Analyzing Unstructured Information. Springer, 2005.

Last update: Svozil Daniel (23.05.2018)
Syllabus -

Introduction to information retrieval, uncertainty, relevance, text document normalization, Zipf's law

Text documents indexing, querying and searching - metrics, vector model - dimensionality reduction, latent semantic indexing

Document and keyword clustering, distance, similarity metrics, centroid, clustering algorithms

Document classification, Bayesian classification, k nearest neighbors, decision trees, metoda support vector machines

The aims and capabilities of text mining, linguistic methods in text mining, tokenization, part-of-speech tagging, named entity recognition, parsing, coreferences

Text mining in information retrieval: document content extraction, automatic document summarization, automatic question answering

Last update: Svozil Daniel (25.05.2018)
Learning resources -

Lecturer materials

Last update: Svozil Daniel (23.05.2018)
Learning outcomes -

Students will know:

  • how to identify documents containing given information
  • how to assign keywords to text documents
  • how to index text documents
  • how to normalize text documents
  • how to categorize text documents
Last update: Svozil Daniel (23.05.2018)
 
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