I want to enter a text, then compare other subsequent texts to it to see whether they are similar enough to warrant the same response.

Count words in each text and report the similarity as number of similar words?

Suppose an array of reference texts linked to potential output texts.

For each word in the new input: 

  If that word is in the reference text, add 1 to the similarity total.

  If a word in the input text's equivalence relations is in the equivalence relations of a word in the reference text, increment the similarity score.

  Store the final similarity total for that reference text, go on to the next reference text, and repeat the above three lines.

  Choose the reference text with the highest similarity total to generate an output.

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Reference texts are trained, or copied and pasted, from the parents of my reddit posts. My responses are linked to the reference texts that provoked my response.

Run the similarity algorithm on all the initial reference texts to see if some can be consolidated. (How to "consolidate"?)

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March 3, 2017:

From https://github.com/sergey-alekseev/lita-answers/blob/master/lib/nlp.rb

class Nlp
  class << self
    # TODO: use real NLP, not this silly chunk of code
    def closest_sentence(string, sentences)
      string_tokens = split_into_tokens(string)
      string_tokens_size = string_tokens.size
      closest_sentence = nil
      closest_sentence_size = 0
      sentences.each do |sentence|
        sentence_tokens = split_into_tokens(sentence)
        common_tokens = string_tokens & sentence_tokens
        bigger_sentence_size = [sentence_tokens.size, string_tokens_size].max
        if bigger_sentence_size - common_tokens.size == 1 && bigger_sentence_size > closest_sentence_size
          closest_sentence = sentence
          closest_sentence_size = bigger_sentence_size
        end
      end
      closest_sentence
    end

    def split_into_tokens(string)
      string.gsub('?', '').downcase.split
    end
  end
end