Persian Text

Research on Persian text processing focuses on overcoming the challenges posed by the language's rich morphology and limited available resources to improve various Natural Language Processing (NLP) tasks. Current efforts concentrate on developing robust models, such as transformer-based architectures (e.g., BERT adaptations), for tasks including semantic similarity measurement, real-word error correction, and emotion recognition in both text and speech. These advancements are crucial for enhancing the quality of Persian digital resources and enabling applications like improved machine translation, information retrieval, and human-computer interaction.