Monolingual Text

Monolingual text analysis focuses on understanding and processing text within a single language, aiming to improve tasks like information retrieval, machine translation, and sentiment analysis. Current research emphasizes developing robust models, often leveraging deep learning architectures like BERT and transformer-based networks, to handle diverse monolingual tasks and improve their performance, particularly for low-resource languages. These advancements are crucial for various applications, including building more effective search engines, improving the accuracy of automated fact-checking systems, and enhancing cross-lingual understanding through improved translation and knowledge transfer techniques. The development of comprehensive benchmarks and datasets for evaluating monolingual models across diverse tasks is also a significant area of focus.