| Title |
AI-driven strategies for NLP challenges in low-resource languages |
| Translation of Title |
Dirbtiniu intelektu pagrįstos strategijos, skirtos spręsti natūralios kalbos apdorojimo iššūkius mažai išteklių turinčioms kalboms. |
| Authors |
Gebremichael Tesfagergish, Senait |
| Full Text |
|
| Pages |
194 |
| Keywords [eng] |
low-resource languages ; AI ; natural language processing ; Amharic ; Tigrinya |
| Abstract [eng] |
This dissertation explores AI-driven solutions to advance Natural Language Processing (NLP) for low-resource languages, with a primary focus on Amharic. While high-resource languages benefit from vast linguistic resources and tools, languages like Amharic lack annotated datasets and computational frameworks, limiting the development of core NLP applications. This research addresses those challenges by developing classification models, implementing transformer-based embeddings, and applying innovative data augmentation methods. It also integrates Explainable AI (XAI) techniques to enhance transparency and trust in model predictions. Key applications examined include sentiment analysis, intent recognition, cyberbullying detection, deepfake recognition, and part-of-speech tagging. The study demonstrates that tailored NLP methods not only improve performance for Amharic but can also be generalized to other underrepresented languages. Overall, this work contributes to the inclusivity and robustness of AI technologies in linguistically diverse digital spaces. |
| Dissertation Institution |
Kauno technologijos universitetas. |
| Type |
Doctoral thesis |
| Language |
English |
| Publication date |
2025 |
