The first couple of steps are actually the easiest. The last paragraph -- which is also your shortest -- is the hardest part, by far.<p>OP, you want to basically extract entities from documents without necessarily knowing them a priori (&quot;companies can use different language to describe these things&quot;). This might be even tougher if companies aren&#x27;t required to report material weaknesses (are they? I&#x27;m unsure). If they don&#x27;t report, then you&#x27;d also likely need to manually code training data for companies for which you have filings.<p>If companies are required to report material weaknesses, then you can likely rely on NLP for this job. Start by building a classifier using material weakness data as your labels and tf-idf vectorized documents as your features (check out parent&#x27;s link, and pick up a general NLP book while you&#x27;re at it).<p>For each of the 50-80 things you mention in your post, OP, you&#x27;ll need to repeat this: (1) obtain labels and (2) figure out a way to train a machine learning algorithm to handle that task for you. The alternative is to rely on heuristics (eg. &quot;I know from past experience that companies using such-and-such word in filings experienced X, so I&#x27;ll encode that in business rules&quot;), which is an ugly way to build something but likely faster than learning about machine learning or NLP.