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Unsupervised machine learning approach for hierarchical graph-based representation of natural language text collections.

Managing big data efficiently is important in various fields, much so when data consists of human-written documents. Recent advances in Natural Language Processing (NLP), particularly LLMs, allowed to solve many task in this domain, despite the high demand for labelled data, compute resources and specialized skills.To tackle these limitations, current study proposed a NLP pipeline to identify topic hierarchies in collections of scientific publications. The work focused on evaluation of available unsupervised machine learning methods and quality metrics in NLP, and development of visualization techniques to build a prototype of the pipeline.Proposed solution is based on the hARTM approach optimized for interpretability. It demonstrated the capacity to infer human-interpretable topic hierarchies from collections of scientific texts and construct meaningful hierarchy of topic-based document representations. The visualization approaches rely on MDS to present inter-document similarity and Sankey plots to show document cluster relatedness within topic hierarchy.Utility was demonstrated on two datasets, focusing on interpretability and meaning of the topic hierarchy and associated topic definitions. Potential application areas include personal education and scientific writing.

Author: Jevgenijs Bodrenko

Supervisor: Irina Jackiva

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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Development of a personal financial management application

Nowadays, financial management is essential in people's lives, as price increases and economic instability require more careful planning of personal finances. Technological development makes this process accessible to everyone, offering various expenditure tracking and budget planning solutions. In the bachelor's thesis, the most popular financial management applications were reviewed, showing that although they offer an intuitive interface and automated budget functions, they also have shortcomings.The goal of the work is to create an application that provides a convenient and digitally enhanced tool for financial planning and tracking. To achieve the goal, tasks included reviewing similar applications, defining requirements, creating the interface and database, developing transaction management and budgeting functionality, and testing the application. All tasks were fully completed.The application uses Python and Flask, SQLite database, HTML, CSS, and JavaScript. As a result, an application with a clear interface was developed, providing manual transaction entry and bank account Excel format upload, editing, and deletion, as well as budget control and transaction visualizations. These functions help users manage their finances more effectively and make better decisions.

Author: Jūlija Ivčenkova

Supervisor: Olga Dribeņeca

Degree: Bachelor

Year: 2024

Work Language: Latvian

Study programme: Computer Science

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