- Email: firstname.lastname@example.org
- Thesis title: Deep Learning and Distributional Semantics for the Quran
In my PhD study, I investigate learning semantic relations between the verses of the Quran using deep neural networks. I consider compositional architectures based on neural networks and train them on the Quranic corpus. Meanwhile, I learn mapping Quran’s verses into embeddings.
In order to better understand how these architectures compare, I conduct experiments on sentence (verse) similarity task. I further extend the embeddings with Hadith or/and ontologies in order to enrich the training data with extra features. My ultimate goal is to help improving the computational analysis of semantic relatedness in specialized texts such as the Quran. My study aims at presenting a new baseline for further work on Quranic semantics analysis, and providing a robust resource for religious scholars, educators, and the public to understand and learn the Quran.