Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Table of contents
1.
Introduction
2.
Definition
3.
Working
4.
Use Cases
4.1.
Voice of Customer Analytics
4.2.
Semantic Search
4.3.
Knowledge Management and Discovery
5.
Features
5.1.
Custom Entity Recognition
5.2.
Custom Classification
5.3.
Entity Recognition
5.4.
PII Identification and Redaction
5.5.
Targeted Sentiment
5.6.
Keyword Extraction
5.7.
Event Detection
5.8.
Language Detection
5.9.
Syntax Analysis
5.10.
Topic Modeling
5.11.
Multiple Language Support
6.
Benefits
7.
Frequently Asked Questions
7.1.
What is Natural Language Processing?
7.2.
What is Amazon Comprehend?
7.3.
Which capabilities are part of Amazon Comprehend?
7.4.
I have to be a natural language processing expert to use Amazon Comprehend?
7.5.
How do I know if Amazon Comprehend is giving accurate results?
8.
Conclusion
Last Updated: Mar 27, 2024

Amazon Comprehend

Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

There's a wealth of untapped information inside documents. The valuable insights found in customer support requests, email surveys, call transcripts, financial forms, and more can significantly improve customers' experiences. But extracting insights from these documents can be expensive, hard to scale, and may take hours to comb through. Amazon Comprehend simplifies this process by using machine learning. It helps you better understand the unseen information you can quickly get up and running with no machine learning experience. Comprehend is trained to identify essential elements in data like language, people, places, and more. It can then automatically organize these text files into categories by relevant terms or topics in real-time. You can accurately and automatically detect customer sentiment in your content and then make immediate decisions based on these findings. It can also be trained to classify documents with subjects or tags that you define using natural language processing techniques. We will learn more about amazon comprehend in this blog.

Definition

Amazon Comprehend uses natural language processing(NLP) to extract information about the content of documents. It generates insights by recognizing entities, essential phrases, language, feelings, and other common elements in a document. By understanding the structure of documents, Amazon Comprehend can help you create new products.

Custom Entity Recognition,  Key phrase Extraction, Custom Classification, Sentiment Analysis, Entity Recognition, and more APIs are available from Amazon Comprehend, allowing you to quickly integrate natural language processing into your applications.

Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

Working

  • Amazon Comprehend employs a pre-trained model to acquire information about a document or a set of documents. You don't need to give training data because this model is constantly trained on an enormous corpus of text.
  • You can use Amazon Comprehend to create custom categorization and entity recognition models.
  • Amazon Comprehend has a built-in model for topic modeling. Topic modeling analyses a corpus of documents and groups them together based on common keywords.
  • Asynchronous and synchronous document processing modes are available in Amazon Comprehend. Process one document or a batch of up to 25 documents in synchronous mode. To process a large number of records, use an asynchronous job.

Use Cases

Voice of Customer Analytics

Based on comments you receive from support calls, emails, social media, and other online channels, you can determine whether consumer sentiment is good, neutral, negative, or mixed.

Semantic Search

You may use Amazon Comprehend to improve your search engine's search results by allowing it to index important terms, entities, and sentiments. This will enable you to concentrate your search on the intent and context of the articles rather than simple keywords.

Knowledge Management and Discovery

You may automatically categorize a collection of documents by topic after analyzing them. The topics can then be used to customize content for your customers.

Features

Custom Entity Recognition

Custom Entity Recognition makes it easy to customize Amazon Comprehend to recognize phrases specific to your domain.

For example, learn from a small set of examples and then train a private, custom model to recognize these terms in any other block of text within PDFs, plain text, or Microsoft Word documents – no machine learning is required.

Custom Classification

The Custom Classification API allows you to quickly create custom text classification models using your company's labels without having to learn machine learning. Custom Classification, for example, can be used by your customer service team to automatically group inbound requests by problem category depending on how the customer has described the issue. 

Entity Recognition

The Entity Recognition API delivers named entities ("People," "Places," "Locations," and so on) that have been automatically classified based on the text input. Sentiment Analysis

The Sentiment Analysis API calculates a text's overall sentiment (Positive, Negative, Neutral, or Mixed).

PII Identification and Redaction

Detect and rewrite/edit personally identifiable information (PII) in customer emails, support issues, product reviews, social media, and more using Amazon Comprehend ML capabilities. You can preserve your privacy and comply with local rules and regulations by redacting PII entities. 

Targeted Sentiment

Targeted sentiment identifies the sentiment (positive, negative, neutral, or mixed) toward things inside the text to deliver more granular sentiment insights. For further information, see Targeted sentiment - Amazon Comprehend.

Keyword Extraction

The Key Extraction API gives key phrases or talking points and a confidence score indicating whether or not this is a key phrase. 

Example:

Sample text:  I am a writer. I like to write about my life experiences. I am more of a personal writer, but sometimes I do commercial writing for sake of livelihood.

Keyphrase

Confidence

a writer

0.99

my life experience

0.97

personal writer

0.99

commercial writing

0.99

Event Detection

Comprehend Events allows you to extract the event structure from a document, reducing pages of text to readily processed data for your AI applications or graph visualization tools to consume. Without any prior NLP experience, you may use this API to answer who-what-when-where inquiries over big document sets at scale.

Language Detection

The Language Detection API detects text written in over 100 languages and returns the dominant language with a confidence score to prove that it is the prevalent language.

Syntax Analysis

Customers can use the Amazon Comprehend Syntax API to analyze text using tokenization and Parts of Speech (PoS), as well as identify word boundaries and labels such as nouns and adjectives.

Topic Modeling

From a collection of documents hosted in Amazon S3, Topic Modeling extracts relevant terms or subjects. It will find the collection's most prevalent topics, group them, and then map which documents belong to which topic.

Multiple Language Support

Text analysis can be performed on German, English, Spanish, Italian, Portuguese, French, Japanese, Korean, Hindi, Arabic, Chinese (simplified), and Chinese (traditional) text using Amazon Comprehend. Customers can use Amazon Translate to convert text into a language supported by Comprehend and then use Comprehend to perform text analysis to create applications in other languages.

Benefits

  • Integrate natural language processing into your apps to make them more powerful.
  • Amazon Comprehend uses deep learning technology to analyze text accurately. Models are regularly trained with new data from multiple domains to increase accuracy.
  • Scalable natural language processing.
  • Amazon Comprehend is meant to function in tandem with other AWS services such as Amazon S3, AWS KMS, and AWS Lambda. Store your documents on Amazon S3 or use Kinesis Data Firehose to examine real-time data.
  • You can already encrypt your input documents with Amazon S3, and Amazon Comprehend takes it further. You can encrypt the output results of your task and the data on the storage volume associated with the compute instance that runs the analytic job by using your own KMS key. As a result, security is significantly improved.
  • There are no minimum costs or commitments with Amazon Comprehend. You pay for the documents you examine and the custom models you develop.

Frequently Asked Questions

What is Natural Language Processing?

Natural language processing (NLP) is a branch of computer science—specifically, a branch of artificial intelligence (AI)—concerning the ability of computers to understand the text and spoken words in the same manner that humans can.

What is Amazon Comprehend?

Amazon Comprehend uses natural language processing(NLP) to extract information about the content of documents. It generates insights by recognizing entities, essential phrases, language, feelings, and other common elements in a document.

Which capabilities are part of Amazon Comprehend?

Custom Entity Recognition, Custom Classification, Key phrase Extraction, Sentiment Analysis, Entity Recognition, and more APIs are available from Amazon Comprehend, allowing you to quickly integrate natural language processing into your applications.

I have to be a natural language processing expert to use Amazon Comprehend?

You don't need to be an expert in natural language processing to use Amazon Comprehend. All you have to do is use Amazon Comprehend's API, and the service will take care of the machine learning required to extract the appropriate data from the text.

How do I know if Amazon Comprehend is giving accurate results?

For each outcome, the service will return a confidence score. Low confidence scores indicate that the service has a quiet confidence level. The score will be closer to one if the service is confident.

Conclusion

Comprehend goes well beyond keyword search or rules-based tagging to classify documents accurately. If any of your content contains personally identifiable information, not only can Comprehend locate it, but it will also redact or mask it and start processing millions of documents in minutes by leveraging the power of machine learning without having to train models from scratch.

This article has discussed Amazon Comprehend- definition, working, use case, and benefits.

After learning, what is Amazon Comprehend in AWS, are you curious to explore more articles on the topic related to Amazon Web Services? We have you covered. Check out Introduction to AWS - Coding Ninjas Coding Ninjas StudioAWS features - Coding Ninjas Coding Ninjas StudioAmazon Web Services | Learn & Practice from Coding Ninjas Studio, and Your Ultimate Job Interview Preparation Guide for Amazon Web Services (AWS), Amazon Hirepro

Refer to guided paths on Coding Ninjas Studio to upskill yourself in Data Structure and AlgorithmsCompetitive ProgrammingJavaScriptSystem Design, and many more. If you want to test your competency in coding, you may check out the mock test series and participate in a contest hosted by Coding Ninjas Studio! But suppose you have just started the learning process and looking for questions asked by tech giants like Amazon, Microsoft, Uber, etc. In that case, you must look at the problemsinterview experiences, and interview bundle for placement preparations.

Nevertheless, you may consider our paid courses to give your career an edge over others!

Do upvote our blogs if you find them helpful and engaging!

Happy learning!

Previous article
Amazon Forecast part 2
Next article
Amazon DevOps Guru
Live masterclass