Word embedding is a way of representing a word that lets words with similar meanings have the same kind of representation. This is a distributed representation of text.
A word embedding is the representation of the text in a learned way in which words with the same meaning get represented in the same manner. This approach to representing the documents and words is one of the major advances of deep learning that takes care of the challenges of the problems of natural processing of language.
One of the main advantages of using the low dimensional and dense vector is computation. In most cases, the neural network toolkit will not work well with the sparse vectors that are high dimensional. The main reason for the dense representation is the power of generalization. If some features could offer the same kind of clues, then it is better to represent them in such a way that it helps to arrest these similarities.
Word embedding vector in the vector space that is pre-defined. Each word goes through word mapping to a single vector, and the vector value is leaned in such a manner that it resembles the true neural network. This is a method that is used in deep learning.
The idea is to approach this idea by using a dense representation for every word. Every word is represented as a real value vector which is tens or even hundreds of the dimension. This is better than the thousand or maybe even millions of dimension that is needed in the case of a sparse representation of words.
The word embedding method is a real-valued vector image for a vocabulary that is predefined and of a fixed size from the text corpus. The learning process gets joined with the model of the neural network on a task like a document classification or in document statistics which is the unsupervised process.
Here we learn about the three-word embedding techniques that are used to learn how to word embedding is done from the textual data.
The embedding layer is a method of word embedding that is learned with the neural network model on the special task of natural language processing like document classification of language modeling.
This method needs training data, and it is slow, but it offers learning that embeds both targets to the NLP task and the special text data.
Word2Vec is a statistical approach for learning and efficient standalone with embedding from a corpus of text. The main benefit of this approach is that the words of high-quality embedding are possible to learn efficiently, which allows larger embedding to be learned from billions of words.
The global vectors for the word representation of the GloVe algorithm are the extension of the previous word2vee method to learn the word vectors efficiently.
There are some options when it comes to using the word embedding on the natural language processing project. These are the options that are explained in this section.
One may choose to understand the word embedding for the problem. This needs ample amounts of text data that ensure that it learns the useful embedded like millions or even words in billions. There are two options again here.
Learn it standalone when the model gets trained to learn the method of embedding with is saved and then used in another model as its part.
Learn jointly is where the embedding gets learned as a small part of a big task of a specific model. This is an excellent approach if you wish to use the embedding in just one task.
Researchers do make the pre-trained word embedding and make them available for free. This is done under a license that permits them so that the embedding can be used in one’s own commercial or academic projects. This can be used on the project instead of doing the embedding right from scratch.
Again there are two options here.
Static is where the embedding stays static, and it gets used only as a component of the model. This is a viable tactic if the embedding works as a perfect fit for the problem and gives you the desired result.
Updated is where the pre-trained embedding gets used to seed any model, and the embedding is then updated jointly in the model training. This is the perfect option if you wish for the model to give you the most and get the maximum embedding on the task.
All that you need to do is to explore the varied options available and test to see which of the methods gives the best result to your problem. You can start first with the fastest method, say the pre-trained method, and use the new embedding if that gives you a better solution to the problem.
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