Amazon Comprehend

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend uses machine learning to help uncover insights and relationships in unstructured data. The service identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; analyzes text using tokenization and parts of speech; and automatically organizes a collection of text files by topic. AutoML capabilities in Amazon Comprehend can be used to build a custom set of entities or text classification models tailored uniquely to an organization’s needs.

• Natural language processing: Amazon Comprehend APIs for entity recognition, sentiment analysis, syntax analysis, key phrase extraction, and language detection can be used to extract insights from natural language text. These requests are measured in units of 100 characters (1 unit = 100 characters), with a 3 unit (300 character) minimum charge per request.
• Personal Identifiable Information (PII): The detect PII API finds locations of chosen Personally Identifiable Information (“PII”) entities inside a document and can be used to create redacted versions of documents. The contains PII API tells you if a document contains the chosen PII or not. These requests are also measured in units of 100 characters (1 unit = 100 characters), with a 3 unit (300 character) minimum charge per request.
• Custom Comprehend: The Custom Classification and Entities APIs can train a custom NLP model to categorize text and extract custom entities. Asynchronous inference requests are measured in units of 100 characters, with a 3 unit (300 character) minimum charge per request. Users are charged $3 per hour for model training (billed by the second) and $0.50 per month for custom model management. For synchronous Custom Classification and Entities inference requests, the user can provision an endpoint with the appropriate throughput, and is charged from the time that the endpoint starts until it is deleted.
• Topic Modeling: Topic Modeling identifies relevant terms or topics from a collection of documents stored in Amazon S3. It will identify the most common topics in the collection and organize them in groups and then map which documents belong to which topic. Users are charged based on the total size of documents processed per job. The first 100 MB is charged a flat rate. Above 100 MB, the charge is per MB.
• Trust and Safety (new): The Comprehend toxicity detection API can be used to detect toxic content from text. Similarly, Comprehend prompt safety classification feature can be used to detect unsafe input prompts to large language models and applications. These requests are measured in units of 100 characters (1 unit = 100 characters), with a 3 unit (300 character) minimum charge per request.