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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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There are methods called "scoring systems" where you give a image scores such as "0.9 stripes, 0.0 red, 0.8 hair, ..." and use those scores to classify objects. It's an older idea, not used to determine if the network is learning. It's not in a standard CNN.
To determine if relevant information is being learned or not, it's standard to use the testing ...
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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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You get what we call high-level features, which are basically abstract representations of the parts that carry information in the image you want to classify.
Imagine you want to classify a car. The image you feed your network could be a car on a road with a driver and trees and clouds, etc. The network, however, if you've trained it to recognize cars, will ...
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It depends on the data. If it is structured like form data, then you might not need AI at all — simple regular expression patterns might be fine. This would apply for example to address data. If you have the word street followed by a colon, followed by some text, it seems fairly obvious that this is the name of a street, and possibly also a house ...
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