What does Natural Language Processing (NLP) primarily use to understand human language?

Natural Language Processing (NLP) primarily relies on machine learning to understand human language. Machine learning allows algorithms to learn from large datasets, improving their ability to identify patterns and make predictions based on the data they are trained on. This approach is particularly effective in dealing with the complexities and nuances of human language, such as syntax, semantics, and context. Machine learning models can adjust and improve their performance over time, enabling more accurate language understanding and generation, which are critical components of NLP applications like language translation, sentiment analysis, and chatbots.

Machine learning encompasses various techniques, including supervised and unsupervised learning, which allow systems to learn from labeled examples or discover patterns in unlabeled data. This adaptability is vital when processing the vast and diverse forms of human communication. Other methods, such as rule-based algorithms, often rely on predefined rules that can be rigid and less effective for understanding the variability in human language. While statistical analysis plays a role in many NLP tasks, machine learning provides a more dynamic and robust framework for interpreting language efficiently. Neural networks, a subset of machine learning models, particularly excel in NLP tasks, but the question specifically highlights machine learning as the overarching technique used in NLP.