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Word vectors will enable breakthroughs in NLP applications and research. They highlight the beauty of neural network DL and the power of learned representations of input data in hidden layers.
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Word Vectors
Word vectors represent a significant leap forward in advancing our ability to analyze relationships across words, sentences, and documents. 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. It is word vectors that make technologies such as speech recognition and machine translation possible. 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).
What Are Word Vectors?
Word vectors are simply vectors of numbers that represent the meaning of a word. For now, that's not very clear, but we'll come back to it in a bit. It is useful, first of all, to consider why word vectors are considered such a leap forward from traditional representations of words.
Traditional approaches to NLP, such as one-hot encoding and bag-of-words models (i.e. using dummy variables to represent the presence or absence of a word in an observation, i.e. a sentence), while useful for some machine learning (ML) tasks, do not capture information about a word's meaning or context. This means that potential relationships, such as contextual closeness, are not captured across collections of words. 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. 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. In essence, traditional approaches to NLP such as one-hot encodings do not capture syntactic (structure) and semantic (meaning) relationships across collections of words and, therefore, represent language in a very naive way.
In contrast, word vectors represent words as multidimensional continuous floating point numbers where semantically similar words are mapped to proximate points in geometric space. In simpler terms, a word vector is a row of real-valued numbers (as opposed to dummy numbers) where each point captures a dimension of the word's meaning and where semantically similar words have similar vectors. This means that words such as wheel and engine should have similar word vectors to the word car (because of the similarity of their meanings), whereas the word banana should be quite distant. Put differently, words that are used in a similar context will be mapped to a proximate vector space (we will get to how these word vectors are created below). The beauty of representing words as vectors is that they lend themselves to mathematical operators. For example, we can add and subtract vectors — the canonical example here is showing that by using word vectors we can determine that:
king - man + woman = queen
In other words, we can subtract one meaning from the word vector for king (i.e. maleness), add another meaning (femaleness), and show that this new word vector (king - man + woman) maps most closely to the word vector for queen.
The numbers in the word vector represent the word's distributed weight across dimensions. In a simplified sense, each dimension represents a meaning and the word's numerical weight on that dimension captures the closeness of its association with and to that meaning. Thus, the semantics of the word are embedded across the dimensions of the vector.
A Simplified Representation of Word Vectors
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https://dzone.com/articles/introduction-to-word-vectors
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