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Recurrent Neural Network for Text Classification
Myška, Vojtěch ; Kolařík, Martin (referee) ; Povoda, Lukáš (advisor)
Thesis deals with the proposal of the neural networks for classification of positive and negative texts. Development took place in the Python programming language. Design of deep neural network models was performed using the Keras high-level API and the TensorFlow numerical computation library. The computations were performed using GPU with support of the CUDA architecture. The final outcome of the thesis is linguistically independent neural network model for classifying texts at character level reaching up to 93,64% accuracy. Training and testing data were provided by multilingual and Yelp databases. The simulations were performed on 1200000 English, 12000 Czech, German and Spanish texts.
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A Classification of a Syndicated Content
Matušov, Izidor ; Očenášek, Pavel (referee) ; Smrčka, Aleš (advisor)
This work deals with a classification of a syndicated content as the possible way of organizing the content. The classification uses algorithms for natural language processing. The main contribution is applying word sense disambiguation algorithm for enhancing the classification, eliminating the learning stage, and using a readability test for improving user experience. The application is implemented as an extensible server-client model. The future work is discussed in the end.
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Scala Programming Language and Its Use for Data Analysis
Kohout, Tomáš ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with comparing the Scala programming language with other commonly used languages for data analysis. These languages are evaluated on the basis of the following categories: data manipulation and visualization, machine learning and concurent processing capabilities. The evaluation then shows the strengths and weaknesses of Scala. The strengths will be demonstrated on application for email categorization.
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Intelligent Mailbox
Pohlídal, Antonín ; Drozd, Michal (referee) ; Chmelař, Petr (advisor)
This master's thesis deals with the use of text classification for sorting of incoming emails. First, there is described the Knowledge Discovery in Databases and there is also analyzed in detail the text classification with selected methods. Further, this thesis describes the email communication and SMTP, POP3 and IMAP protocols. The next part contains design of the system that classifies incoming emails and there are also described realated technologie ie Apache James Server, PostgreSQL and RapidMiner. Further, there is described the implementation of all necessary components. The last part contains an experiments with email server using Enron Dataset.
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Extraction of Semantic Relations from Text
Pospíšil, Milan ; Schmidt, Marek (referee) ; Smrž, Pavel (advisor)
Today exists many semi-structured documents, whitch we want convert to structured form. Goal of this work is create a system, that make this task more automatized. That could be difficult problem, because most of these documents are not generated by computer, so system have to tolerate differences. We also need some semantic understanding, thats why we choose only domain of meeting minutes documents.
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Actual Events Tracker
Odstrčilík, Martin ; Otrusina, Lubomír (referee) ; Kouřil, Jan (advisor)
The goal of the master thesis project was to develop an application for tracking of actual events in the surrounding area of the users. This application should allow the users to view events, create new events and add comments to existing ones. Beyond the implementation of developed application, this project deals with an analysis of the presented problem. The analysis includes a comparison with existing solutions and search for available technologies and frameworks applicable for implementation. Another part inside this work is description of the theory in behind of data classification that is internally used for event and comment analysis. This work also includes a design of appliction including design of user interface, software architecture, database, communication protocol and data classifiers. The main part of this project, the implementation, is described aftewards. At the end of this work, there is a summary of the whole process and also there are given some ideas about enhancing the application in the future.
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Adaptive RSS Reader
Luža, Jindřich ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
Purpose of this balcheor thesis is posibility to enhance common RSS reader by extension, which allowing user filter RSS feed depends on that's classification by content to groups.There is discussed problems in common classification and in text classification. Forth, there is reveal teoretical aspect of RSS format, which is needed to be considered in implementation of RSS reader module and prototype of module. At last, testing of used classifier is stated here.
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Artificial Intelligence Document Classification
Molnár, Ondřej ; Kačic, Matej (referee) ; Třeštíková, Lenka (advisor)
This paper deals with document classification using artificial intelligence. It describes the principles of classification and machine learning. It also introduces AI methods and presents Naive Bayes classification method in detail. Provides practical implementation of the classifier in MS Office and discusses other possible extensions.
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Porovnání open-source nástrojů pro strojové učení
Poliakova, Yevheniia
Poliakova, Y. Comparison of open-source tools for machine learning. Thesis. Brno: Mendel University in Brno, 2022. This work is devoted to the research of accessible open source artificial intelligence. The thesis describes a selected list of available artificial intelligence tools and the use of these tools for specific tasks. The main contribution of the work is the comparison of open-source tools using experiments focused on inductively controlled (supervised, classification) knowledge acquisition from large volumes of text and data. These experiments will be performed using selected open-source tools. The result of the work will be a conclusion about the advantages and disadvantages of the already mentioned platforms, their characteristics in solving specific problems and recommendations for choosing a platform according to the assigned task or data.
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Assessment and implementation of text data preprocessing in neural network models
Ratnasari, Febiyanti
In the realm of text data processing, text preprocessing has traditionally played a significant role. However, with the growing prominence of neural network models and novel representations of textual data, the importance of text preprocessing has been relatively understated. To address this, the present research endeavors to investigate the potential benefits of employing a composite of multiple text data preprocessing techniques in conjunction with a neural network-based text processing model.
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