Text Normalization

Text normalization aims to standardize text by converting non-standard forms (like numerals, abbreviations, and informal spellings) into consistent, canonical representations. Current research focuses on improving normalization accuracy for low-resource languages and less frequent terms, employing techniques like weakly supervised learning, transformer-based language models, and rule-guided neural architectures. These advancements are crucial for enhancing the performance of various natural language processing tasks, including speech recognition, machine translation, and information retrieval, particularly in domains with diverse or historically-influenced writing styles.