Introduction of Word2vec: Learning Word Embeddings
Discover the mechanics behind Word2vec and traditional word representation methods. Learn how neural network-based Word2vec algorithms like skip-gram and continuous bag-of-words improve semantic understanding by generating context-aware numerical word embeddings. Gain insights through training models and visualizing embeddings.
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Overview
In this chapter, we’ll discuss a topic of paramount importance in NLP—Word2vec, a data-driven technique for learning powerful numerical representations (that is, vectors) of words or tokens in a language. Languages are complex. This warrants sound language understanding capabilities in the models we build to solve NLP problems. When transforming words to a numerical representation, a lot of methods aren’t able to sufficiently capture the semantics and contextual information that a word carries. For example, the feature representation of the word “forest” should be very different from ...