title: aligning sentences to corpus and finding mismatches
body: <p>The ideal goal is to correct the output from a speech2text model according to a reference corpus (the actual text). I don't mind using any off the selves tool either in NLP space or ElasticSearch </p>
  
  <p>I have a reference corpus like the following:</p>
  
  <blockquote>
    <p>It is a reliance that has led to a cycle of addiction that has
    destroyed lives it <strong>is a cycle that makes you sick when</strong> you <strong>try to stop
    and potentially takes your</strong> <strong>life when you don't and beyond</strong> its physical
    effects this cycle of addiction also includes constant contact with
    the criminal justice system and not just a cycle of arrests release
    and violation.</p>
  </blockquote>
  
  <p>In fact its much longer ...</p>
  
  <p>On the other hand, I have a set of sentences that are recognized from a speech-2-text model in a CSV files</p>
  
  <pre><code>1, is a cycle that makes you dick when
  2, try two stops and essentially hates your
  3, posses activated
  4, lives when who don't and beyond
  </code></pre>
  
  <p>As you can see there because the speech2text model is not perfect there are errors, for example</p>
  
  <p>1) With references to the corpus these subsentences are misspelled (e.g. dick instead of sick in number the sentence number 1
  2) there are sentences that do not match to the corpus at all - e.g. number 3
  3) putting the sentences together does not cover the whole paragraph.</p>
  
  <p>So basically I wonder what is this task called in the NLP topic, then I can do a better googling, and I appreciate if you name specific functions or examples that I can leverage, e.g. in Space or NLTK or any other tool.</p>
  
  <p><em>edit</em> : * I already have experience with nlp (coursera certificate) - therefore, looking for a concrete answer and/or example rather a scientific paper. This is not a general error correction task or the next work recommendation based on sequential models. </p>