Chapter 3. Word2vec – Learning Word Embeddings

In this chapter, we will discuss a topic of paramount importance in NLP—Word2vec, a technique to learn word embeddings or distributed numerical feature representations (that is, vectors) of words. Learning word representations lies at the very foundation of many NLP tasks because many NLP tasks rely on good feature representations for words that preserve their semantics as well as their context in a language. For example, the feature representation of the word forest should be very different from oven as these words are rarely used in similar contexts, whereas the representations of forest and jungle should be very similar.

Note

Word2vec is called a distributed representation, as the semantics of the word is captured by the activation pattern of the full representation vector, in contrast to a single element of the representation vector (for example, setting a single element in the vector to 1 and rest to 0 for a single...