Document-Level Zero-Shot Relation Extraction from Malaysian English News Article

This thesis develops a method to predict unseen relation between entities in Malaysian English news articles. Due to the lack of suitable datasets and tools, the research creates the MEN-Dataset and develops MEN-spaCy, a specialised Named Entity Recognition (NER) model for Malaysian English. The final part of the work introduces a Relation Extraction framework that identifies unseen relations from input documents or news articles. The development of the dataset, Named Entity Recognition, and Relation Extraction approach creates a foundation for advancing more NLP-related work in Malaysian English.