Is there an algorithm or NN to match two documents? One is a claim description (e.g. a CV or product offer) and another is a requirements description (e.g. vacancy description or RFP). They are not similar, so basically it's not a docs similarity per se.

What's it better embedding to use on document corps (Doc2vec, Word2vec or just TF-IDF? etc) and what kind of further NN architecture would work to basically find a matching scores vector/matrix as output on how do input claim docs match to requirement docs? Or is there exists just any text analitics algorithm or something?

Thanks in advance for help.

| improve this question | | | | |
  • $\begingroup$ "They are not similar, so basically it's not a docs similarity per se." So what is it then? $\endgroup$ – Emre Aug 20 '17 at 18:19
  • $\begingroup$ Matching on some criteria, which stated in requirements. Or do you say should we consider them similar and use similarity approaches? $\endgroup$ – Surgeon Aug 21 '17 at 20:07
  • $\begingroup$ Can you give an example, perhaps with two document snippets and their similarity score? $\endgroup$ – Emre Aug 21 '17 at 20:18
  • $\begingroup$ For example merely any CV like this or this to match job description like this. Sorry, I can't tell you similarity score of them since I've just started playing with different algorythms and approached, working on poc implementation, doing as a pet project apart from my main job. $\endgroup$ – Surgeon Aug 21 '17 at 21:47

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.