Morphosyntactic Analysis

Morphosyntactic analysis focuses on automatically extracting grammatical structure and meaning from text, aiming to improve natural language processing (NLP) for diverse languages. Current research emphasizes developing and applying deep learning models, particularly transformer-based architectures, to achieve accurate lemmatization, part-of-speech tagging, dependency parsing, and other morphosyntactic tasks, often incorporating morphological dictionaries for improved performance, especially in low-resource languages. This work is crucial for advancing cross-lingual NLP, enabling better machine translation, information extraction, and language documentation, as well as facilitating comparative linguistic studies of language acquisition and evolution.