This week you'll learn about Comparison Between BagofWords and Word2Vec.

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The idea of words and sentences being represented in embedding spaces has been heavily discussed in our previous blog. With BagofWords, we had an introductory taste of the potential representational spaces brought to Natural Language Processing (NLP). 

Then we moved to something more refined: Word2Vec. The Word2Vec approach brought a much more novel method of representing each word of our corpus in a much more efficient embedding space. 

Regardless of which algorithm is better, understanding both of these is an important part of the journey you will undergo when you tackle the more complex algorithms in NLP.  

Thus, while both algorithms represent the usage of embeddings/representations, understanding the core differences between the two concepts is necessary moving forward.

The big picture: BagofWords is a simple approach to representing text data. It does not require much preprocessing and is easy to implement. The BagofWords approach counts the number of words in a text. However, it does not take into account the order of the words or the context of the words. 

Word2Vec is a more sophisticated approach to representing text data. It takes into account the order of the words and the context of the words. Word2Vec is more accurate than BagofWords but is a more difficult approach to implement. 

Our blog today takes us through a little journey to understand the differences between these two algorithms. 

How it works: We devise an experiment where we create a similar training environment for both of these algorithms and try to express the learnings in a manner understandable to the human eye. 

Our thoughts: Figuring out paradigms to compare the two algorithms on equal grounds is a very interesting experiment since the essence of these concepts is very different. We have tried to test these on similar grounds so that the results are comparable. 

Yes, but: Our method is not a full-proof method to determine which algorithm is better, as there are some parameters that we cannot touch that pertain to both of these algorithms. 

Stay smart: Do not shy away from further experimentation. 

Click here to read the full tutorial

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