Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs so you can easily integrate natural language processing into your applications. You simply call the Amazon Comprehend APIs in your application and provide the location of the source document or text. The APIs will output entities, key phrases, sentiment, and language in a JSON format, which you can use in your application.

Keyphrase Extraction

The Keyphrase Extraction API returns the key phrases or talking points and a confidence score to support that this is a key phrase.

  • Example: In this example, a customer is comparing a DSLR camera to an instant film camera. The API extracts key phrases and returns a confidence score about the results.

    Sample text: I'm an avid photographer, and I'm primarily found shooting with my DSLR or my instant film camera that I carry around for casual use. While nothing beats my DSLR in power and convenience, there's something magical about my instant film camera. Perhaps it's that you're shooting on actual film, or maybe it's that every shot you take is a unique physical artifact (which is special in today's world of Instagram and Facebook, where photos are a dime a dozen). All I know for sure is that they are incredibly fun to use and peoples' eyes light up when you pull one of these out at a party.

    Keyphrase Confidence
    an avid photographer 0.99
    my DSLR 0.97
    my instant film camera 0.99
    casual use 0.99
    power and convenience 0.94
    actual film 0.99
    every shot 0.92
    a unique physical artifact 0.99
    today 0.91
    world 0.99
    Instagram and Facebook 0.99

Sentiment Analysis

The Sentiment Analysis API returns the overall sentiment of a text (Positive, Negative, Neutral, or Mixed).

Syntax Analysis

The Amazon Comprehend Syntax API enables customers to analyze text using tokenization and Parts of Speech (PoS), and identify word boundaries and labels like nouns and adjectives within the text.

Entity Recognition

The Entity Recognition API returns the named entities ("People," "Places," "Locations," etc.) that are automatically categorized based on the provided text.

Comprehend Medical

Medical Named Entity and Relationship Extraction (NERe)

The Medical NERe API returns the medical information such as medication, medical condition, test, treatment and procedures (TTP), anatomy, and Protected Health Information (PHI). It also identifies relationships between extracted sub-types associated to Medications and TTP. There is also contextual information provided as entity “traits” (negation, or if a diagnosis is a sign or symptom). The table below shows the extracted information with relevant sub-types and entity traits.

To only extract PHI, you can use the Protected Health Information Data Identification (PHId) API.

Medical Ontology Linking

The Medical Ontology Linking APIs identifies medical information and links them to codes and concepts in standard medical ontologies. Medical conditions are linked to ICD-10-CM codes (e.g. “headache” is linked to the “R51” code) with the InferICD10CM API, while medications are linked to RxNorm codes (“Acetaminophine / Codeine” is linked to the “C2341132” cui). The Medical Ontology Linking APIs also detects contextual information as entity traits (e.g. negation).

Custom Entities

Custom Entities allows you to customize Amazon Comprehend to identify terms that are specific to your domain. Using AutoML, Comprehend will learn from a small private index of examples (for example, a list of policy numbers and text in which they are used), and then train a private, custom model to recognize these terms in any other block of text. There are no servers to manage, and no algorithms to master.

Language Detection

The Language Detection API automatically identifies text written in over 100 languages and returns the dominant language with a confidence score to support that a language is dominant.

Custom Classification

The Custom Classification API enables you to easily build custom text classification models using your business-specific labels without learning ML. For example, your customer support organization can use Custom Classification to automatically categorize inbound requests by problem type based on how the customer has described the issue. Creating a custom model is simple. You provide examples of text for each of the labels you want to use, and Comprehend trains on those to create your custom model. No machine learning experience required, you can build your custom model without using a single line of code. An SDK is available for you to integrate your customer classifier into your current applications. With your custom model, it is easy to moderate website comments, triage customer feedback, and organize workgroup documents. Refer to this documentation page for more details.

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.

Multiple language support

Amazon Comprehend can perform text analysis on English, French, German, Italian, Portuguese, and Spanish texts. This lets you build applications that can detect text in multiple languages, convert the text to English, French, German, Italian, Portuguese, and Spanish with Amazon Translate, and then use Amazon Comprehend to perform text analysis.

Learn more about Amazon Comprehend pricing

Visit the pricing page
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