AI/ML, AWS, Cloud Computing

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Custom Entity Recognition with Amazon Comprehend

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In today’s data-driven world, businesses constantly collect and analyze vast amounts of textual data to gain insights, improve customer experiences, and make informed decisions. One essential aspect of text analysis is entity recognition, which involves identifying and classifying entities such as people, organizations, dates, and custom entities specific to your domain. Amazon Web Services (AWS) offers a powerful solution for this task with Amazon Comprehend, a natural language processing (NLP) service. In this blog, we will delve into the world of custom entity recognition using Amazon Comprehend, exploring its capabilities, benefits, and how to set it up.


Entity Recognition:

Entity recognition is a fundamental NLP task that involves identifying and categorizing named entities in a text, such as names of people, places, organizations, dates, and more. Accurate entity recognition can greatly enhance text analysis, enabling businesses to extract valuable insights, improve data quality, and streamline various processes.

Amazon Comprehend: 

Amazon Comprehend is a fully managed NLP service offered by AWS, designed to perform various text analysis tasks, including entity recognition, sentiment analysis, topic modeling, and language detection. It offers pre-trained models for common entity types such as names and dates. Additionally, it supports custom entity recognition, allowing you to identify entities relevant to your specific domain or industry.

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Benefits of Custom Entity Recognition

  • Industry-Specific Insights: Custom entity recognition enables businesses to extract domain-specific information and gain insights that may not be possible with generic entity recognition models.
  • Improved Data Accuracy: By recognizing and classifying custom entities, you can enhance data quality and make more informed decisions based on accurate information.
  • Streamlined Processes: Custom entity recognition can automate data extraction tasks, saving time and resources. For example, in the legal industry, custom entities could include case numbers, legal citations, or specific legal terminology.
  • Enhanced Search and Retrieval: Custom entity recognition can improve search and retrieval capabilities, making finding and organizing information easier.

How Custom Entity Recognition Works with Amazon Comprehend?

  • Custom Entity Recognition Training: To use custom entity recognition with Amazon Comprehend, you must train the service to recognize entities specific to your domain. This involves providing labeled training data that contains examples of the custom entities you want to extract.
  • Annotation Guidelines: It’s essential to create clear annotation guidelines for the human annotators who will label the training data. These guidelines should define the criteria for identifying and classifying custom entities.
  • Labeling Training Data: You must label your training data, highlighting the instances of the custom entities in the text. Amazon Comprehend requires sufficient labeled data to train an accurate model.
  • Model Training: Once you have labeled training data, you can train a custom entity recognition model using Amazon The service will use machine learning algorithms to build a model that identifies custom text entities.
  • Testing and Evaluation: After training the model, testing and evaluating its performance using a separate validation dataset is crucial. You can fine-tune the model to achieve higher accuracy.
  • Deployment: Once the model’s performance is satisfied, you can deploy it to recognize custom entities in new text data.

Use Cases for Custom Entity Recognition

Custom entity recognition can be applied to various use cases across different industries. Here are some examples:

  • Healthcare: Identify and classify medical terms, patient records, and treatment procedures.
  • Finance: Recognize stock symbols, financial instruments, and transaction details.
  • Legal: Extract case numbers, legal citations, and specific legal terminology.
  • Retail: Identify product names, SKU numbers, and customer feedback.
  • Media and Entertainment: Recognize character names, movie titles, and entertainment industry terms.
  • Real Estate: Extract property details, listing information, and location-specific data.

Setting Up Custom Entity Recognition with Amazon Comprehend

  • AWS Account and Permissions: To get started, you need an AWS account. Make sure you have the necessary permissions to use Amazon
  • Data Collection: Gather a sufficient amount of labeled training data that contains examples of the custom entities you want to recognize.
  • Amazon Comprehend Console: Go to the Amazon Comprehend console and create a custom entity recognition project.
  • Define Entity Categories: Define the entity categories you want to recognize, such as product names, customer IDs, or other custom entities relevant to your domain.
  • Training Data Upload: Upload the labeled training data to Amazon
  • Model Training: Start training the custom entity recognition model using your training data.
  • Model Evaluation: Test the model’s performance and fine-tune it if necessary.
  • Model Deployment: Once the model’s accuracy is satisfied, deploy it for use with new text data.

Best Practices for Custom Entity Recognition

  • Annotator Guidelines: Develop clear and detailed annotation guidelines to ensure consistent labeling of training data.
  • Quality Data: Ensure high-quality training data accurately represents the entities you want to recognize.
  • Iterative Training: Custom entity recognition models may require iterative training and fine-tuning to achieve the desired level of accuracy.
  • Continuous Evaluation: Regularly evaluate the model’s performance and adjust as needed.


Custom entity recognition using Amazon Comprehend empowers businesses to unlock valuable insights from their textual data. Whether you’re in healthcare, finance, legal, or any other industry, the ability to identify and classify domain-specific entities can revolutionize data analysis and decision-making processes.

By following best practices and utilizing Amazon Comprehend’s robust capabilities, you can harness the power of custom entity recognition to stay ahead in the era of big data.

In a world where information is king, custom entity recognition is your key to unlocking the full potential of your textual data. With Amazon Comprehend, you can build custom entity recognition models tailored to your needs, helping you gain a competitive edge and make more informed decisions. So, don’t miss out on the opportunity to enhance your text analysis capabilities – get started with custom entity recognition and take your data analysis to the next level.

Drop a query if you have any questions regarding Amazon Comprehend and we will get back to you quickly.

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1. What exactly is custom entity recognition, and how does it differ from regular entity recognition?

ANS: – Custom entity recognition is a specialized form of entity recognition that allows you to identify and classify entities specific to your domain or industry. Unlike regular entity recognition, which typically focuses on common entity types, custom entity recognition lets you tailor the recognition model to your unique needs, extracting domain-specific information.

2. What entities can I recognize with custom entity recognition in Amazon Comprehend?

ANS: – You can recognize many entities, including domain-specific terms, product names, customer IDs, medical codes, legal citations, financial instruments, etc. The possibilities are nearly limitless, depending on your specific use case and requirements.

WRITTEN BY Modi Shubham Rajeshbhai

Shubham Modi is working as a Research Associate - Data and AI/ML in CloudThat. He is a focused and very enthusiastic person, keen to learn new things in Data Science on the Cloud. He has worked on AWS, Azure, Machine Learning, and many more technologies.



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