Lexical Complexity

Lexical complexity, the difficulty of understanding words in a text, is a multifaceted research area aiming to quantify and predict word comprehension challenges across various contexts. Current research focuses on developing computational methods, often employing machine learning models like BERT and transformers, to assess complexity based on features such as word frequency, length, orthographic patterns, and even conversational context in the case of large language models. These advancements have implications for diverse fields, including improving automatic speech recognition, creating personalized language learning tools, and even aiding in the diagnosis of neurological conditions like Alzheimer's disease by analyzing lexical features in language samples.