Explain the features of Amazon Comprehend

In this recipe, we will learn about Amazon Comprehend. We will also learn about the features of Amazon Comprehend.

Recipe Objective - Explain the features of Amazon Comprehend?

The Amazon Comprehend is widely used and is defined as a natural language processing (NLP) service which uses machine learning to find insights and relationships in text and no further machine learning experience is required. Amazon Comprehend uses machine learning to help users uncover the insights and relationships in their unstructured data. Amazon Comprehend service identifies the language of the text, extracts the key phrases, places, people, brands, or events, understands how positive or negative text is, analyzes the text using the tokenization and parts of speech and automatically organizes a collection of text files by the topic. Users can also use the AutoML capabilities in Amazon Comprehend to build the custom set of entities or text classification models which are tailored uniquely to the organization’s needs. Amazon Comprehend console and data access roles can be requested through submission of two AMS Service RFCs: Request access to the Amazon Comprehend by submitting an RFC with the Management, AWS service, Self-provisioned service, Add (ct-3qe6io8t6jtny) change type and this RFC provisions the following IAM role to user's account, customer_comprehend_console_role. And After it's provisioned in the user's account, users must onboard the role in their federation solution. Amazon Comprehend provides a service to create New IAM Role functionality through the Amazon Comprehend console.

Build Log Analytics Application with Spark Streaming and Kafka

Benefits of Amazon Comprehend

  • The Amazon Comprehend uncovers the valuable insights from text in documents, customer support tickets, product reviews, emails, social media feeds, and more and thus provides a machine learning service to find insights. Amazon Comprehend simplifies the document processing workflows by extracting text, key phrases, topics, sentiment, and more from documents such as insurance claims. Amazon Comprehend enables differentiating users' business by training the model to classify documents and identify terms, with no machine learning experience required. Amazon Comprehend protects and controls who has access to sensitive data by identifying and redacting personally Identifiable Information (PII) from the documents.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Comprehend and Features of Amazon Comprehend.

Features of Amazon Comprehend

    • It provides Custom Entity Recognition

Amazon Comprehend provides Custom entity recognition which allows users to customize Amazon Comprehend to identify terms that are specific to the domain. Further using AutoML, Amazon Comprehend will learn from a small set of examples (for example, the list of policy numbers, claim numbers, or SSN), and then train the private, custom model to recognize these terms such as the claim numbers in any other block of text within PDFs, plain text, or Microsoft Word documents – i.e. no machine learning required.

    • It provides Custom Classification

Amazon Comprehend provides the Custom Classification API which enables users to easily build custom text classification models using the business-specific labels without learning Machine Learning. For eg, a User's customer support organization can use Custom Classification to further automatically categorize inbound requests by problem type based on how the customer has described the issue. With the user's custom model, it is further easy to moderate website comments, triage customer feedback, and organize workgroup documents.

    • It provides Entity Recognition

Amazon Comprehend provides Entity Recognition API which returns the named entities ("People," "Places," "Locations," etc.) which are automatically categorized based on the provided text.

    • It provides Sentiment Analysis

Amazon Comprehend provides Sentiment Analysis API which returns the overall sentiment of a text (Positive, Negative, Neutral, or Mixed).

    • It provides PII Identification and Redaction

Amazon Comprehend ML capabilities to detect and redact personally identifiable information (PII) in the customer emails, support tickets, product reviews, social media, and more and No Machine Learning experience is required. For eg, users can analyze support tickets and knowledge articles to detect the PII entities and redact text before users index the documents in the search solution. Further, after that, search solutions are free of the PII entities in documents.

    • It provides Keyphrase Extraction

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

    • It provides Events Detection

Amazon Comprehend provides comprehend Events that lets users extract the event structure from the document, distilling pages of text down to easily processed data for consumption by the Artificial Intelligence applications or graph visualization tools. This API allows users to answer who-what-when-where questions over large document sets, at scale and without prior NLP experience. So, Amazon Comprehend Events can be used to extract granular details about real-world events and associated entities expressed in the unstructured text.

What Users are saying..

profile image

Ed Godalle

Director Data Analytics at EY / EY Tech
linkedin profile url

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More

Relevant Projects

Deploying auto-reply Twitter handle with Kafka, Spark and LSTM
Deploy an Auto-Reply Twitter Handle that replies to query-related tweets with a trackable ticket ID generated based on the query category predicted using LSTM deep learning model.

Airline Dataset Analysis using PySpark GraphFrames in Python
In this PySpark project, you will perform airline dataset analysis using graphframes in Python to find structural motifs, the shortest route between cities, and rank airports with PageRank.

Python and MongoDB Project for Beginners with Source Code-Part 1
In this Python and MongoDB Project, you learn to do data analysis using PyMongo on MongoDB Atlas Cluster.

Azure Data Factory and Databricks End-to-End Project
Azure Data Factory and Databricks End-to-End Project to implement analytics on trip transaction data using Azure Services such as Data Factory, ADLS Gen2, and Databricks, with a focus on data transformation and pipeline resiliency.

Data Processing and Transformation in Hive using Azure VM
Hive Practice Example - Explore hive usage efficiently for data transformation and processing in this big data project using Azure VM.

Build a Real-Time Spark Streaming Pipeline on AWS using Scala
In this Spark Streaming project, you will build a real-time spark streaming pipeline on AWS using Scala and Python.

Build an AWS ETL Data Pipeline in Python on YouTube Data
AWS Project - Learn how to build ETL Data Pipeline in Python on YouTube Data using Athena, Glue and Lambda

Build Streaming Data Pipeline using Azure Stream Analytics
In this Azure Data Engineering Project, you will learn how to build a real-time streaming platform using Azure Stream Analytics, Azure Event Hub, and Azure SQL database.

Build a Data Pipeline in AWS using NiFi, Spark, and ELK Stack
In this AWS Project, you will learn how to build a data pipeline Apache NiFi, Apache Spark, AWS S3, Amazon EMR cluster, Amazon OpenSearch, Logstash and Kibana.

PySpark ETL Project for Real-Time Data Processing
In this PySpark ETL Project, you will learn to build a data pipeline and perform ETL operations for Real-Time Data Processing