You are working with text-heavy insurance documents such as policy forms, endorsements, claim notes, and correspondence. The documents are inconsistent in format, often contain legal language, and may include names, dates, coverage terms, and exclusions mixed into long paragraphs or scanned text. Your goal is to apply modern NLP methods to turn this unstructured content into usable fields and document labels.
How would you apply recent advancements like Natural Language Processing for text-heavy insurance documents appropriately to the problem?
