From the course: Advanced NLP with Python for Machine Learning

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Key takeaways for advanced NLP modeling techniques

Key takeaways for advanced NLP modeling techniques - Python Tutorial

From the course: Advanced NLP with Python for Machine Learning

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Key takeaways for advanced NLP modeling techniques

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- [Present] Let's walk through each of the four techniques we explored and summarize some of the key takeaways. Starting with TF-IDF, this is a fairly simple method that creates document level representations that capture how important a word is to a document within a corpus. It does this without any consideration of context in which a word is used and will return very sparse, very large vectors. And remember, these are stored as sparse matrices. Moving on to word2vec, word2vec is a slightly more sophisticated method that creates word vectors using a shallow two layer neural network. Then we average those word vectors to create a document or text message level representation. This method creates much smaller dense factors. I mentioned TF-IDF creates very sparse vectors with lots of zeros, this is the opposite where it's very dense, meaning very few or no zeros. Word2vec also considers the context in which a word is used…

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