How does machine learning contribute to NLP?

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Answer

Machine learning significantly contributes to NLP by providing the tools to develop models that can learn from data and improve over time. Techniques such as supervised learning, unsupervised learning, and deep learning are used to train NLP models on tasks like language translation, sentiment analysis, and text classification. Machine learning enables NLP systems to recognize patterns, understand context, and make predictions based on input data. The use of neural networks, particularly in deep learning, has enhanced the ability of NLP models to process complex language structures. However, the effectiveness of these models depends on the quality and diversity of the training data.

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