Calculate the similarity distance between documents using pre-trained word2vec model.

• Load a pre-trained word2vec model. Note: You can use Google's pre-trained word2vec model, if you don't have one.

  from gensim.models.keyedvectors import KeyedVectors
  model_path = './data/GoogleNews-vectors-negative300.bin'
  w2v_model = KeyedVectors.load_word2vec_format(model_path, binary=True)

• Once the model is loaded, it can be passed to DocSim class to calculate document similarities.

  from DocSim import DocSim
  ds = DocSim(w2v_model)

• Calculate the similarity score between a source document & a list of target documents.

  source_doc = 'how to delete an invoice'
  target_docs = ['delete a invoice', 'how do i remove an invoice', 'purge an invoice']
  
  # This will return 3 target docs with similarity score
  sim_scores = ds.calculate_similarity(source_doc, target_docs)
  
  print(sim_scores)

• Output is as follows:

    [ {'score': 0.99999994, 'doc': 'delete a invoice'}, 
    {'score': 0.79869318, 'doc': 'how do i remove an invoice'}, 
    {'score': 0.71488398, 'doc': 'purge an invoice'} ]

• Note: You can optionally pass a threshold argument to the calculate_similarity() method to return only the target documents with similarity score above the threshold.

  sim_scores = ds.calculate_similarity(source_doc, target_docs, threshold=0.7)

• Python 3 only
• gensim : to load the word2vec model
• numpy : to calculate similarity scores

The MIT License