How does Watson handle natural language understanding?

Watson handles natural language understanding primarily by employing deep learning and machine learning techniques. This approach allows Watson to analyze and interpret human language in a more nuanced and effective manner than traditional methods. Deep learning models are particularly effective in understanding context, semantics, and syntactic structures within natural language. These models are trained on vast amounts of text data, enabling them to learn patterns, relationships, and linguistic structures that help in tasks like sentiment analysis, entity recognition, and intent classification.

Supervised learning techniques play a role as well in this context, but they are part of the broader machine learning and deep learning approach that Watson utilizes. By leveraging these advanced techniques, Watson can continuously improve its understanding of language and adapt to new data over time, making it more robust and capable in processing natural language queries.

Using rule-based programming, manual encoding of language patterns, or limiting the approach to just supervised learning would not yield the same level of efficiency or adaptability in understanding natural language, as these methods lack the depth and learning capabilities that modern neural networks offer.