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Relational nouns, like brother, mother, capital, advisor, leader are different from sortal words like cup, person, or woman.

They differ in the sense that, one cannot decide if x is a brother unto itself, but rather must consider whether x is a brother in relation to some y. On the other hand, one can decide whether x is a person just by looking at x -- one need not look at other objects and their relationship to x.

Is there a resource listing relational nouns for english?

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    Are "teacher", "butcher" or "lunch" relational nouns?
    – user6726
    Commented Sep 17, 2016 at 23:50
  • I'm not sure how sharp the boundary is, but I don't think those are examples are relational nouns.
    – Edward Newell
    Commented Sep 18, 2016 at 0:35
  • So you're talking about nouns that require something else to establish their meaning? Like a capital of a country, a brother of someone, a leader of a group, and so on?
    – Alenanno
    Commented Sep 18, 2016 at 9:19
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    This isn't necessarily a natural category, so there may not be any good way to describe it. The technical definition in the original paper is "Relational nouns are usually viewed as either requiring non-compositional semantic interpretation, or causing an undesirable proliferation of syntactic rules." In other words, they refer to anything unusual, normally an implicit reference to some other contextual variable or actor. A perfectly reasonable computational definition, but not necessarily congruent to any simple linguistic description.
    – jlawler
    Commented Sep 18, 2016 at 19:03
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    @Close voter(s) Why is this question "too broad"? I could well imagine there is a concise resource in the style of WordNet or a simple list for that, and Edward Newell asked for nothing more than a list.
    – Natalie Clarius
    Commented Sep 19, 2016 at 8:45

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Maybe a wordnet (e.g., the Princeton Wordnet) can be used to extract some relevant nouns. The only disadvantage of the existing wordnets is their rather low coverage of the full lexicon.

But given a starting set, you can try some techniques from machine learning to find more of them in a huge corpus.

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