Which statistical technique was commonly used in early NLP?

Naive Bayes is a statistical technique that was widely used in early Natural Language Processing for its simplicity and effectiveness, particularly in tasks such as text classification and spam detection. The method is based on applying Bayes' theorem, which calculates the probability of a particular class based on the presence of specific features or terms in the text.

One of the reasons for the popularity of Naive Bayes in early NLP is its strong performance even with limited computational resources and training data. It assumes that the presence of a particular feature in a document is independent of the presence of any other feature, which, although a simplifying assumption, works surprisingly well in practice for many text classification tasks.

Moreover, due to its efficiency in processing and training times, Naive Bayes became a go-to method for many early classifiers in NLP, enabling rapid development and deployment of algorithms for text processing applications. This foundational technique paved the way for more complex models that have emerged as natural language processing advanced.