TECHNICAL FIELD

This disclosure relates generally to automatically rectifying in real-time anomalies in natural language processing systems.

BACKGROUND

Natural language processing systems are commonly used in applications such as customer service, chatbots, machine translation, search engines, online or telephonic ordering, or virtual assistants, etc. Many systems with natural language processing or understanding abilities rely on machine learning modules, subsystems, or systems to expand their lexicon and improve accuracy. However, unlike rule-based processing, machine learning takes time. Although anomalies caused by new information, such as new terms or new categories, can be rectified eventually by updating and/or retraining the machine learning model, before the model can be updated or retrained, users of the natural language processing systems can encounter incorrect and inconsistent results, which can damage the user experience. Therefore, a mechanism for rectifying anomalies in real-time that is easy to implement yet effective is desired.

