This work examines the role of both cross-lingual zero-shot learning and data augmentation in detecting hate speech online for low resource set-ups. The proposed solutions for situations where the amount of labeled data ...

Sentiment analysis is an on-going field of research within the realm of Natural Language Processing, in which we wish to accurately assess the sentiment of an author on a given topic. Within this thesis project I construct ...

Qualitative interviews are conducted by researchers to gain a deeper understanding of people’s opinions and perceptions about a specific topic. The analysis of such textual data is an iterative process and often ...

Named Entity Recognition (NER) refers to the task of locating relevant information within text sequences. Within the medical domain, it can benefit applications such as de-identifying patient records or extracting valuable ...

We conduct experiments with BART, a generative language-model architecture, to investigate its capabilities for approximating reasoning by learning from data. For this we use the SimpleLogic dataset, a dataset of satisfiability ...

The complexity of systems have escalated to the point where automated techniques leveraging machine learning methods have become indispensable for log anomaly detection. In this project, carried out in collaboration with ...

This thesis explores the development and evaluation of different approaches to sentiment analysis for the Swedish language, focusing on sentence-level sentiment detection. The study compares traditional rule- and ...