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Feature construction techniques in time-series analysis and single-objective optimization

Author(s): Gašper Petelin (Author), Gregor Papa (Supervisor)

Year: 2025

Type: Doctoral dissertation

Feature construction, encompassing both feature engineering, which involves the manual design of features by domain experts, and representation learning, which refers to the automated discovery of useful data representations during model construction, is a fundamental aspect of machine learning. Its goal is to transform raw data into a more suitable …

Neural approaches to automatic terminology extraction

Author(s): Hanh Thi Hong Tran (Author), Senja Pollak (Supervisor), Antoine Doucet (Co-Supervisor)

Year: 2024

Type: Doctoral dissertation

Automatic terminology extraction, also known as automatic term extraction (ATE), is a natural language processing (NLP) task that identifies specialized terminology from domain-specific corpora. ATE is often used for terminographic tasks (e.g., the creation of specialized dictionaries) and contributes to several complex downstream tasks (e.g., machine translation and information retrieval). …

Bias prediction in multilingual news reporting

Author(s): Swati (Author), Dunja Mladenić (Supervisor)

Year: 2024

Type: Doctoral dissertation

Over the past decade, rapid advancements in natural language processing have opened up new avenues for tackling complex issues such as news bias analysis. This progress has empowered researchers to explore innovative approaches to uncovering the complex biases inherent in news production and coverage processes. News bias, a multifaceted reflection …