Morphological Annotation

Morphological annotation focuses on computationally representing the internal structure of words, encompassing tasks like part-of-speech tagging, lemmatization, and morpheme segmentation. Current research emphasizes developing robust and efficient models, often leveraging deep learning architectures like BERT and XLM-RoBERTa, to handle the complexities of diverse languages, including those with rich morphology or limited resources. This work is crucial for advancing natural language processing applications across various languages and historical periods, improving machine translation, information retrieval, and other language technologies. The creation and standardization of high-quality annotated corpora are also key to progress in the field.