Community Blog Word Embeddings in Natural Language Processing

Word Embeddings in Natural Language Processing

Word embeddings has revolutionized the field of NLP and now it is an industry standard to use the pre-trained models of other large corporations, like Word2Vec.

Word embeddings has revolutionized the field of NLP. At its core, word embeddings are word vectors that each correspond to a single word such that the vectors "mean" the words. This can be demonstrated by certain phenomena such as the vector for king - queen = boy - girl. Word vectors are used to build everything from recommendation engines to chatbots that actually understand the English language.

Another point worth considering is how we obtain word embeddings as no two sets of word embeddings are the same. Word embeddings aren't random; they're generated by training a neural network. A recent powerful word embedding implementation comes from Google named Word2Vec which is trained by predicting words that appear next to other words in a language. For example, for the word "cat", the neural network will predict the words "kitten" and "feline". This intuition of words appearing "near" each other allows us to place them in vector space.

However, it is an industry standard to use the pre-trained models of other large corporations such as Google in order to quickly prototype and to simplify deployment processes. We can download Google's Word2Vec pre-trained word embeddings by running the following command in our working directory:

wget http://magnitude.plasticity.ai/word2vec/GoogleNews-vectors-negative300.magnitude

The word embedding model we downloaded is in a .magnitude format. This format allows us to query the model efficiently using SQL, and is therefore the optimal embedding format for production servers. Since we need to be able to read the .magnitude format, we'll install the pymagnitude package. We'll also install flask to later serve the deep learning predictions made by the model.

pip3 install pymagnitude flask

See more about how to create and deploy a pre-trained Word2Vec deep learning REST API.

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