In many industries, it’s critical to extract custom entities from documents in a timely manner. This can be challenging. Insurance claims, for example, often contain dozens of important attributes (such as dates, names, locations, and reports) sprinkled across lengthy and dense documents. Manually scanning and extracting such information can be error-prone and time-consuming. Rule-based software can help, but ultimately is too rigid to adapt to the many varying document types and layouts.
To help automate and speed up this process, you can use Amazon Comprehend to detect custom entities quickly and accurately by using machine learning (ML). This approach is flexible and accurate, because the system can adapt to new documents by using what it has learned in the past. Until recently, however, this capability could only be applied to plain text documents, which meant that positional information was lost when converting the documents from their native format. To address

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