Contextual Lemmatization

Contextual lemmatization aims to identify the dictionary form (lemma) of a word based on its meaning within a sentence and broader context, a crucial step in natural language processing. Current research focuses on improving accuracy using various approaches, including rule-based systems, statistical models like log-linear models, and deep learning architectures such as Transformers (e.g., BERT, T5, DeBERTa) often incorporating character-level information or hybrid methods combining neural networks with dictionaries and rules. These advancements are vital for improving downstream NLP tasks across diverse languages, particularly those with complex morphology, and enhancing applications like information retrieval, text simplification, and fake news detection.