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Semantic Similarity of Texts
Hajdin, Martin ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This paper deals with the determination of the semantic similarity of texts focusing on categorization of web documents in this case bookmarks. The part of the process is a theoretical overview of methods for system implementation. It describes the design and implementation of the various methods used in the system, too. This paper also deals with the evaluation of various methods where the chosen method are tested according to specified criteria.
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Machine Comprehension Using Commonsense Knowledge
Daniš, Tomáš ; Landini, Federico Nicolás (referee) ; Fajčík, Martin (advisor)
V tejto práci je skumaná schopnosť používať zdravý rozum v moderných systémoch založených na neurónových sieťach. Zdravým rozumom je myslená schopnosť extrahovať z textu fakty, ktoré nie sú priamo spomenuté, ale implikuje ich situácia v texte. Cieľom práce je poskytnúť náhľad na súčasný stav výskumu v tejto oblasti a nájsť sľubné výskumné smery do budúcnosti. V práci je implementovaný jeden z najmodernejších modelov na odpovedanie na otázky a je ďalej použitý na experimenty v rôznych situáciách. Narozdiel od starších prístupov, tento model dosahuje porovnateľné výsledky s najlepšími známymi modelmi aj keď jeho architektúra neobsahuje žiadne prvky zamerané konkrétne na zlepšenie schopnosti zdravo uvažovať. Taktiež boli nájdené štatistické artefakty v populárnej sade dát s otázkami vyžadujúcimi zdravé uvažovanie. Tieto artefakty môžu byť použité štatistickými modelmi na nájdenie správnej odpovede aj v prípadoch, kedy by to nemalo byť možné. Na základe týchto zistení sú v práci poskytnuté odporúčania a návrhy pre výskum do budúcnosti.
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Automated Detection of Hate Speech and Offensive Language
Štajerová, Alžbeta ; Žmolíková, Kateřina (referee) ; Fajčík, Martin (advisor)
This thesis discusses hate speech and offensive language phenomenon, their respective definitions and their occurrence in natural language. It describes previously used methods of solving the detection. An evaluation of available data sets suitable for the problem of detection is provided. The thesis aims to provide additional methods of solving the detection of this issue and it compares the results of these methods. Five models were selected in total. Two of them are focused on feature extraction and the remaining three are neural network models. I have experimentally evaluated the success of the implemented models. The results of this thesis allow for comparison of the typical approaches with the methods leveraging the newest findings in terms of machine learning that are used for the classification of hate speech and offensive language.
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Keyword Suggestion in the Central Portal of Czech Libraries
Balaga, Róbert ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This thesis deals with various methods of keyphrase extraction from documents, specifically focused on documents from the Central Portal of Czech Libraries. Various methods from statistical, linguistic and graph-based methods have been implemented. Also a new method was suggested, that combines the statistical and linguistic approach. Individual methods have been tested and analyzed according to the standard evaluation metrics, with the suggested method achieving recall of 30 percent.
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Deep Neural Networks Used for Customer Support Cases Analysis
Marušic, Marek ; Ryšavý, Ondřej (referee) ; Pluskal, Jan (advisor)
Umelá inteligencia je pozoruhodne populárna v dnešnej dobe, pretože si dokáže poradiť s rôznymi veľmi komplexnými úlohami v odvetviach ako napr. spracovanie obrazu, spracovanie zvuku, spracovanie prirodzeného jazyka a podobne. Keďže Red Hat doteraz už vyriešil obrovksé množstvo zákazníckych požiadavkov počas podpory rôznych produktov. Preto bola navrhnutá myšlienka použiť umelú inteligenciu práve na tieto dáta a docieliť tak zlepšenie a zrýchlenie procesu riešenia zákaznícky požiadavkov. V tejto práci sú popísané použité techniky na spracovanie týchto dát a úlohy, ktoré je možné riešiť pomocou hlbokých neurónových sietí. Taktiež sú v tejto práci popísane rôzne modely, ktoré boli vytvorené počas riešenia tejto práce a snažia sa adresovať rôzne úlohy. Ich výkony sú porovnané na spomínaných úlohách.
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Automatic Adding of Punctuation into Speech Transcript
Ščavnický, Tomáš ; Veselý, Karel (referee) ; Szőke, Igor (advisor)
This thesis deals with the problem of punctuation reconstruction in the output of automatic speech recognition systems. Constrains given on the solutions were applicability on general spoken English language and reasonable accuracy of the punctuation prediction system. Natural language tends to have in some cases non-deterministic nature and usually consists of a large number of grammatic rules. Therefore, a machine learning approach was chosen to solve this problem for its ability to recognize complicated patterns in data. A number of experiments with recurrent neural networks were executed to find the best network architecture for punctuation prediction. Resulting models created during these experiments reach accuracy comparable if not better than the works currently held as state-of-the-art solutions for punctuation reconstruction.
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Word Sense Clustering
Hošták, Viliam Samuel ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This thesis deals with semantic similarity of words. It describes and compares existing models that are currently used for this purpose. It discusses the design and implementation of the system for corpus preprocessing, semantic modelling and retrieval of semantically related words. The system that has been created supports the use of distributional semantic models Word2vec, FastText and Glove.
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Comparison of Annotation Tools
Prexta, Dávid ; Otrusina, Lubomír (referee) ; Dytrych, Jaroslav (advisor)
This work deals with the comparison of annotation tools when working with various data sets, and obtaining the results of comparisons useful for improving the knowledge base of the annotators. The thesis analyzes the existing solutions and their drawbacks, from which the proposals of the new solution are deduced. The other sections deals with the design, implementation and testing of the resulting tool, which is evaluated at the conclusion, and possible future extensions are suggested.
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Automatic Keyword Extraction in Czech
Gallovič, Ľubomír ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This thesis describes design, implementation and testing of application for automatic keyterm extraction from technical texts in czech language. Multiple algorithms for candidate selection, as well as various statistical and linguistic methods for score calculation were implemented. All of these algorithms were analyzed and compared, and best performing ones were chosen to be included in the final version of the program.
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Trie Structures for Large Text Data Processing
Rajčok, Andrej ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This study analyzes natural language processing with emphasis on morphological analysis of inflective languages and systems for named entity recognition. It analyzes effective pattern matching in dictionary by using succint structures and then analyzes practical implementation of succint structures. It describes design and implementation of named entity recognition system and morphological analyzer and compares and test their speed and effectiveness.
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