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What are the exact differences between Word Embedding and Word Vectorization?

I believe "embedding" is simply a subtype of "vectorization" where you use neural networks to learn the vectorization. As stated by Peter above, you can vectorize a text without ...
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Why is max_features ordered by term frequency instead of inverse document frequency

The reason is probably that using the top IDF features would mean selecting the least frequent words, in particular the ones which appear only once and are very frequent. These rare words should ...
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4 votes

Is there a way to map words to their synonyms in tfidf?

TfIdf vectors require much more data than that to be useful, but also don't give you the ability to identify synonyms. To do that with vectors and the amount of data you're working with, you'll need a ...
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1 vote

Effectiveness of tf-idf on documents with repeated keywords

First, please note that TFIDF is a very simplistic weighting method. The principle of the IDF part is indeed to lower the score of words which appear in many documents and give rare words more weight. ...
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