Which of the following is an example of a supervised learning algorithm used in NLP?

Logistic regression is an example of a supervised learning algorithm because it operates on labeled data, where the outcome variable is known. In the context of natural language processing (NLP), supervised learning techniques like logistic regression are often used for classification tasks. For example, it can be used to classify whether a given text is positive or negative or to identify the category of a document.

Supervised learning algorithms learn from a training dataset that contains input-output pairs, training the model to predict the output for unseen data based on the patterns it learned from the training examples. Logistic regression, being a statistical model, provides a probabilistic framework to make predictions based on various features extracted from the text, such as word counts or the presence of specific keywords.

In contrast, the other options listed, such as hierarchical clustering, principal component analysis, and k-means clustering, are examples of unsupervised learning techniques. They do not rely on labeled data for training; instead, they analyze the structure or distribution of data without prior knowledge of the outcomes, making them unsuitable for tasks requiring supervised learning.