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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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Apply a Machine Learning Model to Mitigate Bias in the Future AI-based Recruitment

In the contemporary landscape of Human Resources, the integration of artificial intelligence presents both opportunities and challenges, especially in the field of recruitment encompassing all stages of the process, from candidate sourcing to final selection. However, this integration is not without its challenges. Biased data, originating from historical data or societal prejudices, can present a significant obstacle, potentially perpetuating discriminatory practices. The study "Apply a Machine Learning Model to Mitigate Bias in the Future AI-based Recruitment" aims to comprehensively analyze existing biases from both human and artificial intelligence perspectives within the recruitment process. In its framework, answers to the research questions are sought: what are the existing biases in the recruitment process, both explicit and implicit, and how can biases in the recruitment process be effectively mitigated or eliminated through modeling techniques in future AI-based recruitments systems. Through a data-driven approach and the development of machine learning models, will be discover what kind of biases exist in the selection process and how to mitigate them.

Author: Ērika Todjēre

Supervisor: Jeļena Kijonoka

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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