Semi-Supervised Word Sense Disambiguation for Indonesian Regional Dialects with Data Augmentation and Dictionary-Based Sense Support

Word sense disambiguation (WSD), the process of computationally identifying the appropriate meaning of a word within its context, is a fundamental task in Natural Language Processing (NLP). Effective WSD is crucial for building accurate machine translation systems, information retrieval tools, and sentiment analysis applications, especially when dealing with diverse languages and linguistic variations. While word sense disambiguation research has made substantial progress for resource-rich languages like English, its application to low-resource languages like Indonesian presents a significant opportunity for further development and exploration. In this project, we will develop several WSD benchmark datasets for low-resource Indonesian local languages.