When a child is born, it requires some time to develop the ability to speak and understand. Children only learn the language that people surrounding them will speak. Humans can quickly learn languages on their own, but computers cannot do the same. For instance, you can easily understand the difference between cat and dog, man and woman, and so on. 

This happens because our neural networks are different than the artificial neural networks that machines have. Computers learn languages differently than humans. They use word embedding techniques to understand the language of humans.

What is Word Embedding?

The simple definition of word embedding is converting text into numbers. To get the computer to understand the language, we convert the text into vector form so that computers can develop connections between vectors to words and understand what we are saying. With word embedding, we solve problems related to Natural Language Processing.

Understanding NLP

Natural Language processing helps machines understand and develop the ability to write, read, and listen to what we are saying. Google, DuckDuckGo, and many other browsers use NLP to reduce the language barriers between humans and machines. Furthermore, Microsoft Word and Google Translate are NLP applications.

Algorithms of Word Embedding

Word embedding is a vector representation and requires machine learning techniques and algorithms. These algorithms make use of artificial neural networks and data to generate the connections of different words. For instance, if a model is learning the words “King” and “Queen”, the vectors form will be related to each other. This helps the machine to differentiate yet relate both the words. Below we will understand three common algorithms that you can use in machine learning for word embedding.


Word2Vec is the most popular algorithm for word embedding. This algorithm uses neural networks to learn the embedding more efficiently. This algorithm is actually a series of algorithms. You can use these algorithms for NLP tasks. Word2Vec only uses one hidden layer and connects it with the neural network. All the linear neurons are the hidden layers in the neurons. To train the model, the input layer will include the number of neurons equal to the words in the vocabulary. The size of the output and input layer remain the same. However, the size of the hidden layer is set according to the vectors of the result words’ dimensions. You can perform word embedding with Word2Vec through two methods. In both of these methods, you need artificial neural networks. These methods are:

CBOW or Common Bag of Words

In this method, every word is an input, and the neural network predicts the word that relates to the context. For instance, “I am going home on a bus.” In this example, we will input the word bus in the neural network with context to going or home. Then the machine will generate a vector that connects “traveling to the home” with the bus represented as the source of traveling.

Skim Gram

Skim Gram uses the same trick that a common bag of words or any other machine learning algorithm uses. As we have unlabeled words, the word embedding is essentially semi-supervised learning. In this method, the algorithm uses neighboring words and labels them accordingly. 


Global Vectors for Word Representation or GloVe algorithm is quite similar to Word2Vec. However, the method is a bit different. GloVe only considers the contextual information on the basis of 1-1. This means that GloVe only creates word to word related matrix, which includes the probability P (a | b) viewing the k-word around the word b. 

The main purpose of this technique is to find the representation of the two vectors in a way that generates the log probability of their dot products equal to co-occurrence. They have great results for relating the words in the context to each other.

3.Embedding Layer

This is the first hidden layer of the artificial neural network. This layer should specify three augments. 

Input dim

 This represents the vocabulary size in the text data. For instance, if you have data with integer encoding and values from 0 to 10, then the vocabulary’s size would be 11.

Output dim

They represent the vector space size. The vector space would be of the embedded words. This can be 32, 100 or larger. 

Input Length

This represents the length of the input sequences. For instance, if the words in your input documents are up to 1000, then this value would also be 1000.


Word Embedding is essential for machine learning as this helps computers to understand your language. It contains various algorithms that process words differently, but the main focus of is to help the machine learn languages. Computers cannot understand what we are requesting. Instead, for every word, computers are encoded with a vector representation that relates to other words according to the context.