This master thesis deals with the extraction of relationships between named entities in the text. In the theoretical part of the thesis, the issue of natural language representation for machine processing is discussed. Subsequently, two partial tasks of relationship extraction are defined, namely named entities recognition and classification of relationships between them, including a summary of state-of-the-art solutions. In the practical part of the thesis, system for automatic extraction of relationships between named entities from downloaded pages is designed. The classification of relationships between entities is based on the pre-trained transformers. In this thesis, four pre-trained transformers are compared, namely BERT, XLNet, RoBERTa and ALBERT.

This thesis focuses on the problem of internal guidelines for accounting in a selected company. The first section provides theoretical background of accountancy and internal guidelines. The main tasks of the second part of this work is an analysis of the current company guidelines’ state, identifying imperfections and, consequently, design new or update already existing directives. The aim of the thesis is to increase the efficiency of the management process in the company.

This thesis deals with the creation of internal guidelines in selected company, which are used for bookkeeping. It defines the basic concepts in accounting and characteristics of internal guidelines. It contains proposals selected internal guidelines that will facilitate the orientation of the accounting entity and will improve the efficiency of the process.

In this master thesis are described the history and theoretical background of named-entity recognition and implementation of the system in C++ for named entity recognition and disambiguation. The system uses local disambiguation method and statistics generated from the  Wikilinks web dataset. With implemented system and with alternative implementations are performed various experiments and tests. These experiments show that the system is sufficiently successful and fast. System participates in the Entity Recognition and Disambiguation Challenge 2014.

This master's thesis deals with problems of testing, especially manual and beta testing. Further thesis discuss area of Human - Computer Interaction and looks for possibilities of effective managing of HCI projects. As solution project hosting area is proposed, explained and existing solutions are examined. In practical part of thesis system for managing HCI projects is designed and implemented based on previous research.

Thesis analyses the problem about motorsport discipline hill climb races. Using the findings is designed database structure and created user application together with websites for current result publishing. The work also describes the hardware that is compatible with this application.

This diploma thesis is focused on designing and implementing a web solution to support the operation of a warehouse management system as requested by the system provider. The theoretical part of this paper analyzes the already existing approaches towards the problematic parts of web application development, such as the design of architecture and the method used to record the settings and permissions. The final portal is built using mainly Microsoft technologies. The thesis concludes with an evaluation of applicability of this solution from both technical and economical standpoints.

Today's communication between operators and professional drivers takes place through direct conversations between the parties. This thesis project explores the possibility to support the operators in classifying the topic of incoming communications and which entities are affected through the use of named entity recognition and topic classifications. By developing a synthetic training dataset, a NER model and a topic classification model was developed and evaluated to achieve F1-scores of 71.4 and 61.8 respectively. These results were explained by a low variance in the synthetic dataset in comparison to a transcribed dataset from the real world which included anomalies not represented in the synthetic dataset. The aforementioned models were integrated into the dialogue framework Emora to seamlessly handle the back and forth communication and generating responses.

Transformer-based architectures have in recent years advanced state-of-the-art performance in Natural Language Processing. Researchers have successfully adapted such models to downstream tasks within NLP in a domain-specific setting. This thesis examines the application of these models to the legal domain by doing Named Entity Recognition (NER) in a setting of scarce training data. Three different pre-trained BERT models are fine-tuned on a set of 101 court case documents, whereof one model is pre-trained on legal corpora and the other two on general corpora. Experiments are run to evaluate the models’ predictive performance given smaller or larger quantities of data to fine-tune on. Results show that BERT models work reasonably well for NER with legal data. Unlike many other domain-specific BERT models, the BERT model trained on legal corpora does not outperform the base models. Modest amounts of annotated data seem sufficient for reasonably good performance.

Philosophiae Doctor - PhD / Advancement in biomedical research and continuous growth of scientific literature available in electronic form, calls for innovative methods and tools for information management, knowledge discovery, and data integration. Many biomedical fields such as genomics, proteomics, metabolomics, genetics, and emerging disciplines like systems biology and conceptual biology require synergy between experimental, computational, data mining and text mining technologies. A large amount of biomedical information available in various repositories, such as the US National Library of Medicine Bibliographic Database, emerge as a potential source of textual data for knowledge discovery. Text mining and its application of natural language processing and machine learning technologies to problems of knowledge discovery, is one of the most challenging fields in bioinformatics. This thesis describes and introduces novel methods for knowledge discovery and presents a software system that is able to extract information from biomedical literature, review interesting connections between various biomedical concepts and in so doing, generates new hypotheses. The experimental results obtained by using methods described in this thesis, are compared to currently published results obtained by other methods and a number of case studies are described. This thesis shows how the technology presented can be integrated with the researchers' own knowledge, experimentation and observations for optimal progression of scientific research.