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Suppose that I have a file which has thousands of skills starting from A-Z. Now, I would like to create a model that can group similar skills together (example neural network and SVM can group together). I know that I can use NLP for this problem, but I'm not sure about the algorithm that I can use to get the best result.

I'm new to NLP so any help is greatly appreciated.

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If all you have is the name/word, then I see only two ways:

  1. You can look at word similarity, basically by measuring how similar the spelling is. There are a number of possible metrics (e.g. Levenshtein distance). This will probably not get you very far.
  2. You can look at semantic similarity. You can use pre-trained word embeddings to map the words to a new vector space where you can calculate the distance between the word embeddings, e.g. with word2vec or other implementations. Alternatively, you can use a dictionary/corpus (e.g. https://www.nltk.org/index.html) that provides you a word taxonomy that allows you to compute semantic similarity between words via relationships like synonyms etc. See here for different approaches.
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  • $\begingroup$ Can you show me some example? $\endgroup$ – Siddhant Singh May 9 '19 at 21:05
  • $\begingroup$ what do you mean by pre-trained word embeddings? $\endgroup$ – Siddhant Singh May 11 '19 at 7:33
  • $\begingroup$ if you are unfamiliar with these concepts it's probably best to review them first. if you have a specific question about them, you can open a new question. otherwise your question is too broad to be answered in this type of forum :-) $\endgroup$ – oW_ May 12 '19 at 18:56

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