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.

  • $\begingroup$ "They are not similar, so basically it's not a docs similarity per se." So what is it then? $\endgroup$
    – Emre
    Aug 20, 2017 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? $\endgroup$
    – Yuriy P
    Aug 21, 2017 at 20:07
  • $\begingroup$ Can you give an example, perhaps with two document snippets and their similarity score? $\endgroup$
    – Emre
    Aug 21, 2017 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. $\endgroup$
    – Yuriy P
    Aug 21, 2017 at 21:47

1 Answer 1


One way to interpert your question is matching two documents that have the similar semantic content but might not have the same exact words.

Word Mover’s Distance (WMD) could be useful. WMD is an algorithm for finding the distance between pairs of strings. It is based on word embeddings (e.g., word2vec) which encode the semantic meaning of words into dense vectors.

The WMD distance measures the dissimilarity between two text documents as the minimum amount of distance that the embedded words of one document need to "travel" to reach the embedded words of another document.

For example:

enter image description here Source: "From Word Embeddings To Document Distances" Paper


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