Such encodings often provide sufficient baselines for simple NLP tasks (for example, email spam classifiers), but lack the sophistication for more complex tasks such as translation and speech recognition. For example, a one-hot encoding cannot capture simple relationships, such as determining that the words 'dog' and 'cat' both refer to animals that are often discussed in the context of household pets. This means that potential relationships, such as contextual closeness, are not captured across collections of words. a sentence), while useful for some machine learning (ML) tasks, do not capture information about a word's meaning or context.

using dummy variables to represent the presence or absence of a word in an observation, i.e. Traditional approaches to NLP, such as one-hot encoding and bag-of-words models (i.e. It is useful, first of all, to consider why word vectors are considered such a leap forward from traditional representations of words. For now, that's not very clear, but we'll come back to it in a bit. Word vectors are simply vectors of numbers that represent the meaning of a word. There are many excellent explanations of word vectors, but in this one, I want to make the concept accessible to data and research people who aren't very familiar with natural language processing (NLP). It is word vectors that make technologies such as speech recognition and machine translation possible. In doing so, they advance technology by providing machines with much more information about words than has previously been possible using traditional representations of words. Word vectors represent a significant leap forward in advancing our ability to analyze relationships across words, sentences, and documents.

Another word for add to