Morphological Analyzer

Morphological analyzers are computational tools that break down words into their constituent morphemes (meaningful units) and identify their grammatical roles, a crucial step in natural language processing (NLP). Current research emphasizes improving accuracy and efficiency, particularly for low-resource languages, using diverse approaches including deep learning models (like Bi-LSTMs and BERT variants), finite-state machines, and pattern-based methods. These advancements are vital for improving NLP applications across various languages, enabling better machine translation, information retrieval, and language documentation efforts, especially for endangered languages.