Morphological Tokenization

Morphological tokenization focuses on segmenting text into units that align with linguistic morphemes (meaningful units), unlike purely statistical methods that may split words arbitrarily. Current research emphasizes developing unsupervised and deep learning models, such as those based on Transformer architectures and semi-Markov models, to achieve more accurate and linguistically informed tokenization, often evaluated through intrinsic (morpheme boundary accuracy) and extrinsic (downstream task performance) metrics. This improved tokenization is crucial for enhancing the performance of natural language processing tasks, particularly in morphologically rich languages, and has broader applications in areas like image analysis and material science where identifying meaningful structural units is key.