Morpheme Based

Morpheme-based approaches in natural language processing (NLP) focus on analyzing and utilizing the smallest meaningful units of language (morphemes) to improve language model performance and understanding. Current research investigates how morpheme-aware tokenization methods, often integrated with subword techniques like Byte Pair Encoding (BPE), can enhance various NLP tasks, particularly in morphologically rich languages. This work highlights the trade-off between linguistic accuracy and computational efficiency, with studies exploring the impact of different morpheme segmentation granularities on tasks such as parsing, named entity recognition, and machine translation. The ultimate goal is to leverage morphological information to build more robust and accurate language models, improving applications across diverse languages and domains.