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Natural language processing (NLP) faces several disadvantages, including challenges in understanding context, ambiguity, and nuances in human language, which can lead to misinterpretations. Additionally, NLP models often require large amounts of annotated data for training, which can be resource-intensive to obtain. They may also exhibit biases present in training data, leading to unfair or inaccurate outputs. Furthermore, NLP systems struggle with languages and dialects that have less representation in available datasets.

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