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Though word-embedding is primarily a language modeling tool, it also acts as a feature extraction method because it helps transform raw data (characters in text documents) to a meaningful alignment of word vectors in the embedding space that the model can work with more effectively (than other traditional methods such as TF-IDF, Bag of Words, etc, on a large ...


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I think you guys are playing on semantics. If you consider feature extraction to be an unlearned preprocessing step to get inputs for your model, then no, word embeddings are not a feature extraction technique (examples here would be BoW counts, n-gram features, etc) If you consider feature extraction to be any form of conversion from text to a set of ...


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