title: Matching set of words with set of sentences in python nlp
body: <p>I have a use case where I want to match one list of words with a list of sentences and bring the most relevant sentences</p>
  
  <p>I am working in python. What I have already tried is using KMeans where we cluster our set of documents into the clusters and then predict the sentence that in which structure it resides. But in my case I have already available list of words available.</p>
  
  <pre><code>def getMostRelevantSentences():
      Sentences = ["This is the most beautiful place in the world.",
              "This man has more skills to show in cricket than any other game.",
              "Hi there! how was your ladakh trip last month?",
              "Isn’t cricket supposed to be a team sport? I feel people should decide first whether cricket is a team game or an individual sport."]
  
      words = ["cricket","sports","team","play","match"]
  
      #TODO: now this should return me the 2nd and last item from the Sentences list as the words list mostly matches with them
  </code></pre>
  
  <p>So from the above code I want to return the sentences which are closely matching with the words provided. I don't want to use the supervised machine learning here. Any help will be appreciated.</p>