You can customize Amazon Comprehend for your specific requirements without the skillset required to build machine learning-based NLP solutions. Using automatic machine learning, or AutoML, Comprehend Custom builds customized NLP models on your behalf, using training data that you provide.
Input document processing – Amazon Comprehend supports one-step document processing for custom classification and custom entity recognition. For example, you can input a mix of plain text documents and semi-structured documents (such as PDF documents, Microsoft Word documents, and images) to a custom analysis job. For more information, see Document processing.
Custom classification – Create custom classification models (classifiers) to organize your documents into your own categories. For each classification label, provide a set of documents that best represent that label and train your classifier on it. Once trained, a classifier can be used on any number of unlabeled document sets. You can use the console for a code-free experience or install the latest AWS SDK. For more information, see Custom classification.
Custom entity recognition – Create custom entity recognition models (recognizers) that can analyze text for your specific terms and noun-based phrases. You can train recognizers to extract terms like policy numbers, or phrases that imply a customer escalation. To train the model, you provide a list of the entities and a set of documents that contain them. Once the model is trained, you can submit analysis jobs against it to extract their custom entities. For more information, see Custom entity recognition.
