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I am not even an amateur in linguistics, especially semantics. I want to use this idea in computational linguistics that I am also new there. The idea is how to deal with nouns that become more concrete as we describe them using adjectives. For example, when we use car in a sentence, it is kind of abstract. But when we use red car or fast red car it is more concrete. I want to know the terminology about this idea and I also need some resources to read.

I want to try to use some ideas from semantics in the field of word embeddings and try to make NLP a little more intuitive. The basic idea here is statistical semantics and I want to see if I can apply it in this special case.

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  • Fast red car is more specific than car, but not more concrete. Are you sure you know what you want to accomplish?
    – jlawler
    Nov 24 at 20:12
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"Car" picks out a set, the set of cars. It is not particularly abstract, but it is large. "Red car" picks out the intersection of red things and cars, which is a subset of the set of cars. It is not less abstract, but it is a smaller set. So "red" can be said to restrict the reference of the nominal expression to which it attaches. It is an "interactive" adjective used here with a "restrictive" reading.

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