Answer / Quresh Ahamed

Different NLU methods have their strengths and weaknesses. Rule-based systems rely on explicit rules to understand language, making them reliable but less flexible. Statistical models, such as Naive Bayes and Support Vector Machines, learn patterns from data without explicitly defining rules, providing good performance for some tasks but poorer results for others. Deep learning models like RNNs, LSTMs, and transformers can handle more complex language structures but require large amounts of annotated data to train effectively.

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