Word Embedding is a language modeling method applied for mapping words to vectors of real figures. It represents words or expressions in vector space with several dimensions. Word embeddings can be generated utilizing varied methodologies like neural networks, co-occurrence matrix, probabilistic models, etc.
Why Word Embeddings?
What makes word embedding different and significant from other methods is that it works on the boundaries of other Bag of words and other methodologies, Many points that make word embedding better than others are-
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A better understanding of Words and Sentences than different ways in NLP, also known as linguistic analysis.
Word Embedding reduces the dimensions of the dataset better than other NLP approaches and therefore performs better.
Takes smaller implementation time or in other words, is quickly in training than others as it doesn’t take a humongous measure of weights for training as others.
It doesn't follow the way of the Sparse matrix, thus making it better for computational reasons.
Limitations of Word Embeddings
In addition to the dataset or corpus, further training is needed, still, the limitation lies in the significant growth in memory space.
As you can windup from the illustration, the correlation matrix representation of three sentences took so significant vector space.
Keras library
The Keras library is one of the most famed and generally applied deep learning libraries for Python that's built on top of TensorFlow.
To apply word embeddings, the Keras library contains a layer called Embedding (). The embedding layer is administered in the form of a class in Keras and is typically applied as a first layer in the successive model for NLP tasks.
The embedding layer can be applied to perform three tasks in Keras
It can be applied to get word embeddings and save the resulting model.
It can be used to learn the word embeddings in extension to accomplish the NLP tasks like text bracket, sentiment analysis, etc.
It can be applied to load pre-trained word embeddings and use them in a new model.
You may sustain undesirable impulses at the end of the training dataset isn't correctly pre-processed.
Conclusion
Here, we learned about word embedding python , limitation of word embedding and Keras library.
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