|
| |
|
|
Representation of Text and Its Influence on Categorization
Šabatka, Ondřej ; Chmelař, Petr (referee) ; Bartík, Vladimír (advisor)
The thesis deals with machine processing of textual data. In the theoretical part, issues related to natural language processing are described and different ways of pre-processing and representation of text are also introduced. The thesis also focuses on the usage of N-grams as features for document representation and describes some algorithms used for their extraction. The next part includes an outline of classification methods used. In the practical part, an application for pre-processing and creation of different textual data representations is suggested and implemented. Within the experiments made, the influence of these representations on accuracy of classification algorithms is analysed.
|
|
| |
|
|
Text Data Clustering
Leixner, Petr ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
Process of text data clustering can be used to analysis, navigation and structure large sets of texts or hypertext documents. The basic idea is to group the documents into a set of clusters on the basis of their similarity. The well-known methods of text clustering, however, do not really solve the specific problems of text clustering like high dimensionality of the input data, very large size of the databases and understandability of the cluster description. This work deals with mentioned problems and describes the modern method of text data clustering based on the use of frequent term sets, which tries to solve deficiencies of other clustering methods.
|
|
|
Mining of Textual Data from the Web for Speech Recognition
Kubalík, Jakub ; Plchot, Oldřich (referee) ; Mikolov, Tomáš (advisor)
Prvotním cílem tohoto projektu bylo prostudovat problematiku jazykového modelování pro rozpoznávání řeči a techniky pro získávání textových dat z Webu. Text představuje základní techniky rozpoznávání řeči a detailněji popisuje jazykové modely založené na statistických metodách. Zvláště se práce zabývá kriterii pro vyhodnocení kvality jazykových modelů a systémů pro rozpoznávání řeči. Text dále popisuje modely a techniky dolování dat, zvláště vyhledávání informací. Dále jsou představeny problémy spojené se získávání dat z webu, a v kontrastu s tím je představen vyhledávač Google. Součástí projektu byl návrh a implementace systému pro získávání textu z webu, jehož detailnímu popisu je věnována náležitá pozornost. Nicméně, hlavním cílem práce bylo ověřit, zda data získaná z Webu mohou mít nějaký přínos pro rozpoznávání řeči. Popsané techniky se tak snaží najít optimální způsob, jak data získaná z Webu použít pro zlepšení ukázkových jazykových modelů, ale i modelů nasazených v reálných rozpoznávacích systémech.
|
|
|
Textual Data Clustering Methods
Miloš, Roman ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
Clustering of text data is one of tasks of text mining. It divides documents into the different categories that are based on their similarities. These categories help to easily search in the documents. This thesis describes the current methods that are used for the text document clustering. From these methods we chose Simultaneous keyword identification and clustering of text documents (SKWIC). It should achieve better results than the standard clustering algorithms such as k-means. There is designed and implemented an application for this algorithm. In the end, we compare SKWIC with a k-means algorithm.
|
|
| |
|
|
Keyword Extraction from Documents
Matička, Jiří ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
This thesis pursues an automated extraction of keywords from documents. Its goal is to design and implement an application which will be able to extract an appropriate set of keywords related to the contents of the document. The major requirements for the application are speed and accuracy. That is why the first part of the thesis talks about already developed principles and a detailed classification based on various criteria. The second part is focused on choosing and a thorough functional describing of one of the methods which should have been used for extracting the keywords. The next parts contain a detailed draft of the application and its implementation. Finally, the last chapter is particularly important due to testing the application on a group of text documents and evaluating final results of the extraction process.
|
|
|
Using of Data Mining Method for Analysis of Social Networks
Novosad, Andrej ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
Thesis discusses data mining the social media. It gives an introduction about the topic of data mining and possible mining methods. Thesis also explores social media and social networks, what are they able to offer and what problems do they bring. Three different APIs of three social networking sites are examined with their opportunities they provide for data mining. Techniques of text mining and document classification are explored. An implementation of a web application that mines data from social site Twitter using the algorithm SVM is being described. Implemented application is classifying tweets based on their text where classes represent tweets' continents of origin. Several experiments executed both in RapidMiner software and in implemented web application are then proposed and their results examined.
|
|
|
Extraction of Semantic Relations from Text
Schmidt, Marek ; Burget, Radek (referee) ; Smrž, Pavel (advisor)
Extraction of semantic relations from English text is the topic of this thesis. It focuses on exploitation of a dependency parser. A method based on syntactic patterns is proposed and evaluated in addition to evaluation of several statistical methods over syntactic features. It applies the methods for extraction of a hypernymy relation and evaluates it on the WordNet thesaurus. A system for extraction of semantic relations from text is designed and implemented based on these methods.
|
guest ::
