Expanding Malaysian English Dataset with Human-in-the-Loop Annotation for Entity and Relation Recognition

Malaysian English, being a low-resource creole language, presents unique challenges for natural language processing tasks such as Named Entity Recognition (NER) and Relational Extraction (RE). In this paper, we propose a methodology utilizing Human-in-the-Loop (HITL) Annotation to address these challenges and enhance the annotation process for NER and RE tasks in Malaysian English. By implementing this methodology, we effectively expanded the MEN Dataset from 6,061 entities to 12,456 entities and from 4,095 relation instances to 7,794 relation instances. This promising outcome serves as an encouragement to expand resources for any low-resource language by implementing the discussed methodology.