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This thesis introduces a new optimization method based on deep learning, designed for data influenced by random processes. The main contribution of this method is the combination of advanced noise reduction techniques with recurrent neural network models, which helps to prevent the common problem of overfitting seen when there is …

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). …

The thesis addresses a novel representation learning framework, combining neural and symbolic text representations, and demonstrates its utility for tackling diverse natural language processing problems. The proposed approach, avoiding the deficiencies of purely symbolic and purely neural methods, can be applied for the generation of efficient text representations. Its usefulness …

A healthy diet is becoming increasingly relevant as recognizing dietary deficiencies often leads to actionable results that can improve the individual’s overall health. However, to identify areas of potential improvement, tracking food intake is necessary. Manual methods have traditionally been used to perform this tracking, but these methods have a …

Robots that are supposed to perform human-like tasks must possess appropriate skills to carry them out. In unstructured environments and for complex tasks, these skills are difficult to pre-program due to the complexity of the real world. It is therefore advantageous if robots have the ability to acquire the necessary …