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Stemming Methods Used in Text Mining
Adámek, Tomáš ; Chmelař, Petr (referee) ; Bartík, Vladimír (advisor)
The main theme of this master's thesis is a description of text mining. This document is specialized to English texts and their automatic data preprocessing. The main part of this thesis analyses various stemming algorithms (Lovins, Porter and Paice/Husk). Stemming is a procedure for automatic conflating semantically related terms together via the use of rule sets. Next part of this thesis describes design of an application for various types of stemming algorithms. Application is based on the Java platform with using of graphic library Swing and MVC architecture. Next chapter contains description of implementation of the application and stemming algorithms. In the last part of this master's thesis experiments with stemming algorithms and comparing the algorithm from viewpoint to the results of classification the text are described.
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Improved Prediction of Social Tags Using Data Mining
Harár, Pavol ; Galáž, Zoltán (referee) ; Kříž, Jiří (advisor)
This master’s thesis deals with using Text mining as a method to predict tags of articles. It describes the iterative way of handling big data files, parsing the data, cleaning the data and scoring of terms in article using TF-IDF. It describes in detail the flow of program written in programming language Python 3.4.3. The result of processing more than 1 million articles from Wikipedia database is a dictionary of English terms. By using this dictionary one is capable of determining the most important terms from article in corpus of articles. Relevancy of consequent tags proves the method used in this case.
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Determination of basic form of words
Šanda, Pavel ; Burget, Radim (referee) ; Karásek, Jan (advisor)
Lemmatization is an important preprocessing step for many applications of text mining. Lemmatization process is similar to the stemming process, with the difference that determines not only the word stem, but it´s trying to determines the basic form of the word using the methods Brute Force and Suffix Stripping. The main aim of this paper is to present methods for algorithmic improvements Czech lemmatization. The created training set of data are content of this paper and can be freely used for student and academic works dealing with similar problematics.
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Recognition of emotions in Czech texts
Červenec, Radek ; Smékal, Zdeněk (referee) ; Burget, Radim (advisor)
With advances in information and communication technologies over the past few years, the amount of information stored in the form of electronic text documents has been rapidly growing. Since the human abilities to effectively process and analyze large amounts of information are limited, there is an increasing demand for tools enabling to automatically analyze these documents and benefit from their emotional content. These kinds of systems have extensive applications. The purpose of this work is to design and implement a system for identifying expression of emotions in Czech texts. The proposed system is based mainly on machine learning methods and therefore design and creation of a training set is described as well. The training set is eventually utilized to create a model of classifier using the SVM. For the purpose of improving classification results, additional components were integrated into the system, such as lexical database, lemmatizer or derived keyword dictionary. The thesis also presents results of text documents classification into defined emotion classes and evaluates various approaches to categorization.
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Creation of family trees by using modern ICT technology
Hošek, Martin ; Samec, Marek (advisor) ; Jelínek, Ivan (referee)
This bachelor's thesis deals with the topic of family trees, their creation by using electronic resources, software and web resources and ways of looking up the information about the history of the family. The aim is to create a methodological guide, how to start with creating a family tree and how to create it. In the first part, important concepts are theoretically defined as well as methods for looking up necessary information. The second part describes the procedure for creating a family tree. The final section summarizes how to proceed during the creation of family tree and what to avoid.
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Options of automated categorization of contracts
Bereš, Miroslav ; Jelínek, Ivan (advisor) ; Oškera, Radek (referee)
My bachelor thesis is focused on automatic categorization. The main goal is to examine actual approaches in automatic categorization, propose methodology for an experiment and perform the experiment. The experiment is done in order to measure success rate of automatic categorization with use of machine learning. It is performed on contracts obtained from public administration's web pages. The bachelor is divided into two parts, theoretical part and the experiment. First one focuses on analyzing theory which explains the subject matter, there are also described current approaches in automatic categorization. Second part describes methodology proposal of the experiment and performing of the experiment. During the process of the experiment, there are created models that are applied on control group. The experiment's outputs are categorized documents. These documents are used to monitor the success rate of automatic categorization. In order to measure the success rate, there is software called Apache OpenNLP used in this experiment. The theoretical part and proposal of the methodology are written based on studying foreign professional literature, mostly obtained from electronic and information sources.
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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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Automatizace generování stopslov
Krupník, Jiří
This diploma thesis focuses its point on automatization of stopwords generation as one method of pre-processing a textual documents. It analyses an influence of stopwords removal to a result of data mining tasks (classification and clustering). First the text mining techniques and frequently used algorithms are described. Methods of creating domain specific lists of stopwords are described to detail. In the end the results of large collections of text files testing and implementation methods are presented and discussed.
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Computational Systems for Selection and Priorization of Candidate Genes that Underlie Human Hereditary Disease
Adášková, Jana
The aim of this paper is to present an overview of six independent computational methods for the selection and prioritization of candidate genes for human diseases and, rather than selecting a best method, to offer the prospective user a better understanding of the inputs, outputs and functionality of each available method. A survey of these methods also offers the bioinformatics community an opportunity to assess the efficacy of current computational approaches to disease gene identification, and informs future directions for research in this field.
Fulltext: content.csg -  PDF
Plný tet: 0373338 - PDF
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Extrakce informací z textu
Michalko, Boris ; Labský, Martin (advisor) ; Svátek, Vojtěch (referee) ; Nováček, Jan (referee)
Cieľom tejto práce je preskúmať dostupné systémy pre extrakciu informácií a možnosti ich použitia v projekte MedIEQ. Teoretickú časť obsahuje úvod do oblasti extrakcie informácií. Popisujem účel, potreby a použitie a vzťah k iným úlohám spracovania prirodzeného jazyka. Prechádzam históriou, nedávnym vývojom, meraním výkonnosti a jeho kritikou. Taktiež popisujem všeobecnú architektúru IE systému a základné úlohy, ktoré má riešiť, s dôrazom na extrakciu entít. V praktickej časti sa nacházda prehľad algoritmov používaných v systémoch pre extrakciu informácií. Opisujem oba typy algoritmov ? pravidlové aj štatistické. V ďalšej kapitole je zoznam a krátky popis existujúcich voľných systémov. Nakoniec robím vlastný experiment s dvomi systémami ? LingPipe a GATE na vybraných korpusoch. Meriam rôzne výkonnostné štatistiky. Taktiež som vytvoril malý slovník a regulárny výraz pre email aby som demonštroval taktiež pravidlá pre extrahovanie určitých špecifických informácií.
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