Text normalization and inverse text normalization using weighted finite-state transducers and neural language models
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United States of America
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Feb 18, 2025
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Feb 29, 2024
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Aug 31, 2022
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Aug 31, 2022
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Abstract
Systems and methods provide for text normalization or inverse text normalization using a hybrid language system that combines rule-based processing with neural or learned processing. For example, a hybrid rule-based and neural approach identifies semiotic tokens within a textual input and generates a set of potential plain-text conversions of the semiotic tokens. The plain-text conversions are weighted and evaluated by a trained language model that rescores the plain-text conversion based on context to identify a highest scoring plain-text conversion for further processing within a language system pipeline.
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