Getting Started with Named Entity Recognition in Azure AI Language Services

Named Entity Recognition (NER) is a vital component in natural language processing (NLP), enabling the extraction of key information such as names of people, organizations, locations, dates, and more from unstructured text. Azure AI Language Services provide robust tools for NER tasks, making it easier for developers to implement NER functionalities into their applications seamlessly.

In this article, we will provide you with an overview of NER in Azure AI Language Services and guide you on how to get started with this exciting feature using Python.

Understanding Named Entity Recognition 

Before diving into implementation, let’s briefly understand what Named Entity Recognition entails. NER involves identifying and classifying named entities mentioned in text into predefined categories such as person names, organization names, location names, etc. It plays a crucial role in various NLP applications like information extraction, question answering, sentiment analysis, and more.

Getting Started with Named Entity Recognition in Azure AI Language Services

To get started with Named Entity Recognition (NER) in Azure AI Language Services, you have two options that you can choose from:

  1. Language Studio: This web-based platform allows you to try entity linking with text examples without requiring an Azure account. It’s a great option for exploring the capabilities of NER without any setup. You can experiment with different text samples and see how NER identifies and categorizes entities in real-time.
  2. Integration into Applications: If you’re ready to integrate NER into your own applications, you can use the REST API or the client library available in various programming languages such as C#, Java, JavaScript, and Python. This allows you to leverage NER’s capabilities within your existing projects and workflows.

Getting Started with Python Code Example

To demonstrate how to use Azure AI Language Services for Named Entity Recognition, we’ll walk through a Python code example using the Azure SDK. Below is a step-by-step guide:

Step 1: Set Up Azure Resources

Before you begin, ensure you have the following:

  • An Azure subscription: You can create one for free if you don’t have it already.
  • Python 3.7 or later installed on your machine.

Once you have your Azure subscription:

  • Navigate to the Azure portal and create a Language resource.
  • Note down the key and endpoint provided after the resource deployment.
  • Select “Go to resource” once the deployment is complete.

You’ll use the key and endpoint later in your code to connect to the API. If you’re just trying out the service, you can opt for the free pricing tier (Free F0) initially and upgrade to a paid tier for production usage. Note that to utilize the Analyze feature, you’ll need a Language resource with the standard (S) pricing tier.

Step 2: Installing the Client Library

After setting up your Azure resources, install the client library using the following command after installing Python:

pip install azure-ai-textanalytics==5.2.0

Step 3: Implement Named Entity Recognition

Now, let’s write a Python script to perform Named Entity Recognition using Azure AI Language Services. Below is a basic example:

And here’s the output you should expect when running the script:

Responsible AI and Data Security in Named Entity Recognition 

Utilizing Named Entity Recognition (NER) in Azure AI Language Services requires prioritizing responsible AI and data security. This entails ethically deploying AI technologies with transparency, fairness, and accountability. Azure AI Language Services offers a transparency note outlining NER model behaviors, limitations, and potential biases. Integration involves adhering to legal and ethical guidelines to ensure responsible use without infringing on data privacy or intellectual property rights. Data privacy and security are paramount; Azure AI Language Services employs industry-leading security practices to safeguard sensitive information. Leveraging Azure’s secure infrastructure instills trust in data protection.

For further exploration and integration into your projects, refer to the official Azure AI Language Services documentation and resources. Happy coding!

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