What is one-hot encoding used for in natural language processing?

One-hot encoding is a technique commonly used in natural language processing (NLP) to represent words or tokens in a format that can be processed by machine learning algorithms. The primary goal of one-hot encoding is to convert categorical data, such as words, into a numerical format that captures their uniqueness without imposing any ordinal relationships.

The correct answer is that one-hot encoding transforms a word into a vector where one corresponds to its position in the vocabulary. In this representation, each word is assigned a unique index based on its position in a predefined vocabulary list. For instance, if the vocabulary consists of five words, one-hot encoding would represent each word as a binary vector of length five, with a '1' at the position corresponding to the word's index and '0's elsewhere. This way of encoding ensures that no two words are similar (as they have orthogonal vectors) and helps in avoiding biases associated with the meanings of the words.

This method is particularly useful because it allows algorithms to process text without introducing assumptions about the relationships or distances between words. Since it treats each word independently, one-hot encoding helps maintain the distinct nature of each token in the context of NLP tasks.