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Information Retrieval in Text Data
Tkadlčík, Luboš ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis researches the issue of text data mining and information retrieval. It describes the most common representations of text documents and retrieval strategies. The aim of this thesis is design and implementation of application, which realises information retrieval via vector space model. The application implements three different ways of similarity calculation: cosine measure, the Jaccard coefficient and the Dice coefficient. Achieved results are assessed. Possible continuance of the project is outlined.
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Information Retrieval in Research Portals
Ďulík, Jan ; Smrž, Pavel (referee) ; Schmidt, Marek (advisor)
This paper deals with the information retrieval in research portals with the intention of the retrieval in scientific publications. We define concepts related to the information retrieval, classification and knowledge representation. We also present existing search tools used as the initial inspiration for the design of the search intergace. Futhermore we describe the implementation as well as the process of collecting sample data. In the last chapter we discuss usability of the developed web application.
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Digital Library Information Retrieval
Hochmal, Petr ; Rychlý, Marek (referee) ; Chmelař, Petr (advisor)
This thesis deals with methods of information retrieval. Firstly, it describes models of information retrieval and methods of retrieval evaluation. Then it brings closer the principles of the input text processing for IR with use of stopword list and stemmer. Furthermore, it shows the way of the query expansion with synonyms using the thesaurus, methods of handling phrases appearance in queries and introduces the idea of ranking documents by the degree of phrase occurrence similarity in documents. In the second part of this thesis is described the design of whole IR system with using vector model, query expansion with synonyms and phrases handling. This system has been implemented in C# as the application for retrieving and administration of the documents in digital libraries. The effectiveness of this system has been evaluated at the end of this thesis by several tests.
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Wikipedia Page Classification
Suchý, Ondřej ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The goal of this paper is to design and implement a system for selection of Wikipedia articles relevant to a given topic in order to reduce the amount of memory taken by its offline version. The solution of this problem was achieved with use of methods from information retrieval and theirs implementation using Elasticsearch search engine. The system tries to determine the area of user's interest by given keywords and make a selection of articles from that area. This is achieved by measuring of similarity of articles and adding all articles from frequent categories in the selection. The sizes of the output files for queries over Simple English Wikipedia are usually below 30 MB.
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Challenges in Providing Unpublished Research Data in Biomedical Engineering to Grey Literature Repositories
Francová, Pavla ; Krueger, Stephanie
Regardless of the scientific field or focus, every researcher produces during his or her career a multitude of unpublished research data such as laboratory diaries, grant proposals, images etc. Although making such data more accessible undoubtedly has value for researchers, they are currently not shared in open repositories. Why are researchers still reluctant to actively use grey literature repositories for unpublished contextual materials and data? Authors will discuss how scientists might be encouraged to add such material to grey literature repositories and specific examples of unpublished research data will be shown in connection to model scientifi c project.
Fulltext: idr-940_3 -  PDF
Slides: idr-940_1 - PDF; idr-940_2 - PDF
Video: idr-940_4 -  MP4
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Hledání sémantické informace v textových datech s využitím latentní analýzy
Řezníček, Pavel
The first part of thesis focuses on theoretical introduction to the methods of text mining -- Information retrieval, classification and clustering. LSA method is presented as an advanced model for representing textual data. Furthermore, the work describes source data and methods for their preprocessing and preparation used to enhance the effectiveness of text mining methods. For each chosen text mining method there are defined evaluation metrics and used already existing, or newly implemented, programs are presented. The results of experiments comparing the effects of different preprocessing type and use of different models of the source data are then demonstrated and discussed in the conclusion.
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