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I want to find all material nouns in a sentence. Using pos_tag set of nltk only give NN, NNS, NNP etc. Is there any existing work to find out material nouns or how can we solve this problem?

Edit: An example will be

Input: Don't forget to bring water bottle, a blanket and shoes **during your visit.**

Output: [water bottle, blanket, shoes]

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  • $\begingroup$ Welcom to DS Stack Exchange. Please elaborate more in your question, could you add more details? $\endgroup$
    – Leevo
    Commented Apr 24, 2020 at 14:31
  • $\begingroup$ A simple approach would be to elaborate a list of materials then check for those nouns. There would be some mistakes (material nouns that have other uses, new materials that don't appears in your list). $\endgroup$ Commented Apr 24, 2020 at 17:06
  • $\begingroup$ @Leevo added an example of I/O.To Icrmorin, elaboration is not possible since list is infinite in the universe. $\endgroup$
    – CKM
    Commented Apr 25, 2020 at 3:38
  • $\begingroup$ Does your corpus have a fixed set of materials? Or they could be about anything? It's possible there is not training data for such a task. One option would be to manually label few hundreds of sentences, and train a first classifier on those. $\endgroup$
    – Leevo
    Commented Apr 25, 2020 at 10:42
  • $\begingroup$ Alternatively, you could run a PoS tagger on the nltk categories (NN, NNS, NNP), and the work by subtraction, i.e. finding which, among those, do not correspond to material nouns. Do you think that's possible? $\endgroup$
    – Leevo
    Commented Apr 25, 2020 at 10:44

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