Quickstart: Detecting named entities (NER)

Reference documentation | More samples | Package (NuGet) | Library source code

Use this quickstart to create a Named Entity Recognition (NER) application with the client library for .NET. In the following example, you will create a C# application that can identify recognized entities in text.

Prerequisites

• Azure subscription - Create one for trial
• The Visual Studio IDE

Setting up

Create an Azure resource

To use the code sample below, you'll need to deploy an Azure resource. This resource will contain a key and endpoint you'll use to authenticate the API calls you send to the Language service.

1. Use the following link to create a language resource using the Azure portal. You will need to sign in using your Azure subscription.

2. On the Select additional features screen that appears, select Continue to create your resource.

   

3. In the Create language screen, provide the following information:

   Detail	Description
   Subscription	The subscription account that your resource will be associated with. Select your Azure subscription from the drop-down menu.
   Resource group	A resource group is a container that stores the resources you create. Select Create new to create a new resource group.
   Region	The location of your Language resource. Different regions may introduce latency depending on your physical location, but have no impact on the runtime availability of your resource. For this quickstart, either select an available region near you, or choose China East 2.
   Name	The name for your Language resource. This name will also be used to create an endpoint URL that your applications will use to send API requests.
   Pricing tier	The pricing tier for your Language resource. You can use the Free F0 tier to try the service and upgrade later to a paid tier for production.

   

4. Make sure the Responsible AI Notice checkbox is checked.

5. Select Review + Create at the bottom of the page.

6. In the screen that appears, make sure the validation has passed, and that you entered your information correctly. Then select Create.

Get your key and endpoint

Next you will need the key and endpoint from the resource to connect your application to the API. You'll paste your key and endpoint into the code later in the quickstart.

1. After the Language resource deploys successfully, click the Go to Resource button under Next Steps.

   

2. On the screen for your resource, select Keys and endpoint on the left navigation menu. You will use one of your keys and your endpoint in the steps below.

   

Create environment variables

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

• To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Create a new .NET Core application

Using the Visual Studio IDE, create a new .NET Core console app. This creates a "Hello World" project with a single C# source file: program.cs.

Install the client library by right-clicking on the solution in the Solution Explorer and selecting Manage NuGet Packages. In the package manager that opens select Browse and search for Azure.AI.TextAnalytics. Select version 5.2.0, and then Install. You can also use the Package Manager Console.

Code example

Copy the following code into your program.cs file and run the code.

using Azure;
using System;
using Azure.AI.TextAnalytics;

namespace Example
{
    class Program
    {
        // This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
        static string languageKey = Environment.GetEnvironmentVariable("LANGUAGE_KEY");
        static string languageEndpoint = Environment.GetEnvironmentVariable("LANGUAGE_ENDPOINT");

        private static readonly AzureKeyCredential credentials = new AzureKeyCredential(languageKey);
        private static readonly Uri endpoint = new Uri(languageEndpoint);
        
        // Example method for extracting named entities from text 
        static void EntityRecognitionExample(TextAnalyticsClient client)
        {
            var response = client.RecognizeEntities("I had a wonderful trip to Seattle last week.");
            Console.WriteLine("Named Entities:");
            foreach (var entity in response.Value)
            {
                Console.WriteLine($"\tText: {entity.Text},\tCategory: {entity.Category},\tSub-Category: {entity.SubCategory}");
                Console.WriteLine($"\t\tScore: {entity.ConfidenceScore:F2},\tLength: {entity.Length},\tOffset: {entity.Offset}\n");
            }
        }

        static void Main(string[] args)
        {
            var client = new TextAnalyticsClient(endpoint, credentials);
            EntityRecognitionExample(client);

            Console.Write("Press any key to exit.");
            Console.ReadKey();
        }

    }
}

Output

Named Entities:
        Text: trip,     Category: Event,        Sub-Category:
                Score: 0.74,    Length: 4,      Offset: 18

        Text: Seattle,  Category: Location,     Sub-Category: GPE
                Score: 1.00,    Length: 7,      Offset: 26

        Text: last week,        Category: DateTime,     Sub-Category: DateRange
                Score: 0.80,    Length: 9,      Offset: 34

Reference documentation | More samples | Package (Maven) | Library source code

Use this quickstart to create a Named Entity Recognition (NER) application with the client library for Java. In the following example, you will create a Java application that can identify recognized entities in text.

Prerequisites

• Azure subscription - Create one for trial
• Java Development Kit (JDK) with version 8 or above

Setting up

Create an Azure resource

To use the code sample below, you'll need to deploy an Azure resource. This resource will contain a key and endpoint you'll use to authenticate the API calls you send to the Language service.

1. Use the following link to create a language resource using the Azure portal. You will need to sign in using your Azure subscription.

2. On the Select additional features screen that appears, select Continue to create your resource.

   

3. In the Create language screen, provide the following information:

   Detail	Description
   Subscription	The subscription account that your resource will be associated with. Select your Azure subscription from the drop-down menu.
   Resource group	A resource group is a container that stores the resources you create. Select Create new to create a new resource group.
   Region	The location of your Language resource. Different regions may introduce latency depending on your physical location, but have no impact on the runtime availability of your resource. For this quickstart, either select an available region near you, or choose China East 2.
   Name	The name for your Language resource. This name will also be used to create an endpoint URL that your applications will use to send API requests.
   Pricing tier	The pricing tier for your Language resource. You can use the Free F0 tier to try the service and upgrade later to a paid tier for production.

   

4. Make sure the Responsible AI Notice checkbox is checked.

5. Select Review + Create at the bottom of the page.

6. In the screen that appears, make sure the validation has passed, and that you entered your information correctly. Then select Create.

Get your key and endpoint

Next you will need the key and endpoint from the resource to connect your application to the API. You'll paste your key and endpoint into the code later in the quickstart.

1. After the Language resource deploys successfully, click the Go to Resource button under Next Steps.

   

2. On the screen for your resource, select Keys and endpoint on the left navigation menu. You will use one of your keys and your endpoint in the steps below.

   

Create environment variables

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

• To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Add the client library

Create a Maven project in your preferred IDE or development environment. Then add the following dependency to your project's pom.xml file. You can find the implementation syntax for other build tools online.

<dependencies>
     <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-ai-textanalytics</artifactId>
        <version>5.2.0</version>
    </dependency>
</dependencies>

Code example

Create a Java file named Example.java. Open the file and copy the below code. Then run the code.

import com.azure.core.credential.AzureKeyCredential;
import com.azure.ai.textanalytics.models.*;
import com.azure.ai.textanalytics.TextAnalyticsClientBuilder;
import com.azure.ai.textanalytics.TextAnalyticsClient;

public class Example {

    // This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
    private static String languageKey = System.getenv("LANGUAGE_KEY");
    private static String languageEndpoint = System.getenv("LANGUAGE_ENDPOINT");

    public static void main(String[] args) {
        TextAnalyticsClient client = authenticateClient(languageKey, languageEndpoint);
        recognizeEntitiesExample(client);
    }
    // Method to authenticate the client object with your key and endpoint
    static TextAnalyticsClient authenticateClient(String key, String endpoint) {
        return new TextAnalyticsClientBuilder()
                .credential(new AzureKeyCredential(key))
                .endpoint(endpoint)
                .buildClient();
    }
    // Example method for recognizing entities in text
    static void recognizeEntitiesExample(TextAnalyticsClient client)
    {
        // The text that needs to be analyzed.
        String text = "I had a wonderful trip to Seattle last week.";
    
        for (CategorizedEntity entity : client.recognizeEntities(text)) {
            System.out.printf(
                "Recognized entity: %s, entity category: %s, entity sub-category: %s, score: %s, offset: %s, length: %s.%n",
                entity.getText(),
                entity.getCategory(),
                entity.getSubcategory(),
                entity.getConfidenceScore(),
                entity.getOffset(),
                entity.getLength());
        }
    }
}

Output

Recognized entity: trip, entity category: Event, entity sub-category: null, score: 0.74, offset: 18, length: 4.
Recognized entity: Seattle, entity category: Location, entity sub-category: GPE, score: 1.0, offset: 26, length: 7.
Recognized entity: last week, entity category: DateTime, entity sub-category: DateRange, score: 0.8, offset: 34, length: 9.

Reference documentation | More samples | Package (npm) | Library source code

Use this quickstart to create a Named Entity Recognition (NER) application with the client library for Node.js. In the following example, you create a JavaScript application that can identify recognized entities in text.

Prerequisites

• Azure subscription - Create one for trial
• Node.js v14 LTS or later

Setting up

Create an Azure resource

To use the code sample below, you'll need to deploy an Azure resource. This resource will contain a key and endpoint you'll use to authenticate the API calls you send to the Language service.

1. Use the following link to create a language resource using the Azure portal. You will need to sign in using your Azure subscription.

2. On the Select additional features screen that appears, select Continue to create your resource.

   

3. In the Create language screen, provide the following information:

   Detail	Description
   Subscription	The subscription account that your resource will be associated with. Select your Azure subscription from the drop-down menu.
   Resource group	A resource group is a container that stores the resources you create. Select Create new to create a new resource group.
   Region	The location of your Language resource. Different regions may introduce latency depending on your physical location, but have no impact on the runtime availability of your resource. For this quickstart, either select an available region near you, or choose China East 2.
   Name	The name for your Language resource. This name will also be used to create an endpoint URL that your applications will use to send API requests.
   Pricing tier	The pricing tier for your Language resource. You can use the Free F0 tier to try the service and upgrade later to a paid tier for production.

   

4. Make sure the Responsible AI Notice checkbox is checked.

5. Select Review + Create at the bottom of the page.

6. In the screen that appears, make sure the validation has passed, and that you entered your information correctly. Then select Create.

Get your key and endpoint

Next you will need the key and endpoint from the resource to connect your application to the API. You'll paste your key and endpoint into the code later in the quickstart.

1. After the Language resource deploys successfully, click the Go to Resource button under Next Steps.

   

2. On the screen for your resource, select Keys and endpoint on the left navigation menu. You will use one of your keys and your endpoint in the steps below.

   

Create environment variables

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

• To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Create a new Node.js application

In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it.

mkdir myapp 

cd myapp

Run the npm init command to create a node application with a package.json file.

npm init

Install the client library

Install the npm package:

npm install @azure/ai-language-text

Code example

Open the file and copy the below code. Then run the code.

"use strict";

const { TextAnalyticsClient, AzureKeyCredential } = require("@azure/ai-text-analytics");

// This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
const key = process.env.LANGUAGE_KEY;
const endpoint = process.env.LANGUAGE_ENDPOINT;

//an example document for entity recognition
const documents = [ "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800"];

//example of how to use the client library to recognize entities in a document.
async function main() {
    console.log("== NER sample ==");
  
    const client = new TextAnalysisClient(endpoint, new AzureKeyCredential(key));
  
    const results = await client.analyze("EntityRecognition", documents);
  
    for (const result of results) {
      console.log(`- Document ${result.id}`);
      if (!result.error) {
        console.log("\tRecognized Entities:");
        for (const entity of result.entities) {
          console.log(`\t- Entity ${entity.text} of type ${entity.category}`);
        }
      } else console.error("\tError:", result.error);
    }
  }

//call the main function
main().catch((err) => {
    console.error("The sample encountered an error:", err);
});

Output

Document ID: 0
        Name: Microsoft         Category: Organization  Subcategory: N/A
        Score: 0.29
        Name: Bill Gates        Category: Person        Subcategory: N/A
        Score: 0.78
        Name: Paul Allen        Category: Person        Subcategory: N/A
        Score: 0.82
        Name: April 4, 1975     Category: DateTime      Subcategory: Date
        Score: 0.8
        Name: 8800      Category: Quantity      Subcategory: Number
        Score: 0.8
Document ID: 1
        Name: 21        Category: Quantity      Subcategory: Number
        Score: 0.8
        Name: Seattle   Category: Location      Subcategory: GPE
        Score: 0.25

Reference documentation | More samples | Package (PyPi) | Library source code

Use this quickstart to create a Named Entity Recognition (NER) application with the client library for Python. In the following example, you create a Python application that can identify recognized entities in text.

Prerequisites

• Azure subscription - Create one for trial
• Python 3.8 or later

Setting up

Install the client library

After installing Python, you can install the client library with:

pip install azure-ai-textanalytics==5.2.0

Code example

Create a new Python file and copy the below code. Then run the code.

# This example requires environment variables named "LANGUAGE_KEY" and "LANGUAGE_ENDPOINT"
language_key = os.environ.get('LANGUAGE_KEY')
language_endpoint = os.environ.get('LANGUAGE_ENDPOINT')

from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

# Authenticate the client using your key and endpoint 
def authenticate_client():
    ta_credential = AzureKeyCredential(language_key)
    text_analytics_client = TextAnalyticsClient(
            endpoint=language_endpoint, 
            credential=ta_credential)
    return text_analytics_client

client = authenticate_client()

# Example function for recognizing entities from text
def entity_recognition_example(client):

    try:
        documents = ["I had a wonderful trip to Seattle last week."]
        result = client.recognize_entities(documents = documents)[0]

        print("Named Entities:\n")
        for entity in result.entities:
            print("\tText: \t", entity.text, "\tCategory: \t", entity.category, "\tSubCategory: \t", entity.subcategory,
                    "\n\tConfidence Score: \t", round(entity.confidence_score, 2), "\tLength: \t", entity.length, "\tOffset: \t", entity.offset, "\n")

    except Exception as err:
        print("Encountered exception. {}".format(err))
entity_recognition_example(client)

Output

Named Entities:

    Text:    trip   Category:        Event  SubCategory:     None
    Confidence Score:        0.74   Length:          4      Offset:          18

    Text:    Seattle        Category:        Location       SubCategory:     GPE
    Confidence Score:        1.0    Length:          7      Offset:          26

    Text:    last week      Category:        DateTime       SubCategory:     DateRange
    Confidence Score:        0.8    Length:          9      Offset:          34

Reference documentation

Use this quickstart to send Named Entity Recognition (NER) requests using the REST API. In the following example, you'll use cURL to identify recognized entities in text.

Prerequisites

• Azure subscription - Create one for trial

Setting up

Create an Azure resource

To use the code sample below, you'll need to deploy an Azure resource. This resource will contain a key and endpoint you'll use to authenticate the API calls you send to the Language service.

1. Use the following link to create a language resource using the Azure portal. You will need to sign in using your Azure subscription.

2. On the Select additional features screen that appears, select Continue to create your resource.

   

3. In the Create language screen, provide the following information:

   Detail	Description
   Subscription	The subscription account that your resource will be associated with. Select your Azure subscription from the drop-down menu.
   Resource group	A resource group is a container that stores the resources you create. Select Create new to create a new resource group.
   Region	The location of your Language resource. Different regions may introduce latency depending on your physical location, but have no impact on the runtime availability of your resource. For this quickstart, either select an available region near you, or choose China East 2.
   Name	The name for your Language resource. This name will also be used to create an endpoint URL that your applications will use to send API requests.
   Pricing tier	The pricing tier for your Language resource. You can use the Free F0 tier to try the service and upgrade later to a paid tier for production.

   

4. Make sure the Responsible AI Notice checkbox is checked.

5. Select Review + Create at the bottom of the page.

6. In the screen that appears, make sure the validation has passed, and that you entered your information correctly. Then select Create.

Get your key and endpoint

Next you will need the key and endpoint from the resource to connect your application to the API. You'll paste your key and endpoint into the code later in the quickstart.

1. After the Language resource deploys successfully, click the Go to Resource button under Next Steps.

   

2. On the screen for your resource, select Keys and endpoint on the left navigation menu. You will use one of your keys and your endpoint in the steps below.

   

Create environment variables

Your application must be authenticated to send API requests. For production, use a secure way of storing and accessing your credentials. In this example, you will write your credentials to environment variables on the local machine running the application.

To set the environment variable for your Language resource key, open a console window, and follow the instructions for your operating system and development environment.

• To set the LANGUAGE_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the LANGUAGE_ENDPOINT environment variable, replace your-endpoint with the endpoint for your resource.

setx LANGUAGE_KEY your-key

setx LANGUAGE_ENDPOINT your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

export LANGUAGE_KEY=your-key

export LANGUAGE_ENDPOINT=your-endpoint

Create a JSON file with the example request body

In a code editor, create a new file named test_ner_payload.json and copy the following JSON example. This example request will be sent to the API in the next step.

{
    "kind": "EntityRecognition",
    "parameters": {
        "modelVersion": "latest"
    },
    "analysisInput":{
        "documents":[
            {
                "id":"1",
                "language": "en",
                "text": "I had a wonderful trip to Seattle last week."
            }
        ]
    }
}

Save test_ner_payload.json somewhere on your computer. For example, your desktop.

Send a named entity recognition API request

Use the following commands to send the API request using the program you're using. Copy the command into your terminal, and run it.

curl -X POST "%LANGUAGE_ENDPOINT%/language/:analyze-text?api-version=2022-05-01" ^
-H "Content-Type: application/json" ^
-H "Ocp-Apim-Subscription-Key: %LANGUAGE_KEY%" ^
-d "@C:\Users\<myaccount>\Desktop\test_ner_payload.json"

curl.exe -X POST $env:LANGUAGE_ENDPOINT/language/:analyze-text?api-version=2022-05-01 `
-H "Content-Type: application/json" `
-H "Ocp-Apim-Subscription-Key: $env:LANGUAGE_KEY" `
-d "@C:\Users\<myaccount>\Desktop\test_ner_payload.json"

curl -X POST $LANGUAGE_ENDPOINT/language/:analyze-text?api-version=2022-05-01 \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY" \
-d "@/home/mydir/test_ner_payload.json"

curl -X POST $LANGUAGE_ENDPOINT/language/:analyze-text?api-version=2022-05-01 \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: $LANGUAGE_KEY" \
-d "@/home/mydir/test_ner_payload.json"

JSON response

{
	"kind": "EntityRecognitionResults",
	"results": {
		"documents": [{
			"id": "1",
			"entities": [{
				"text": "trip",
				"category": "Event",
				"offset": 18,
				"length": 4,
				"confidenceScore": 0.74
			}, {
				"text": "Seattle",
				"category": "Location",
				"subcategory": "GPE",
				"offset": 26,
				"length": 7,
				"confidenceScore": 1.0
			}, {
				"text": "last week",
				"category": "DateTime",
				"subcategory": "DateRange",
				"offset": 34,
				"length": 9,
				"confidenceScore": 0.8
			}],
			"warnings": []
		}],
		"errors": [],
		"modelVersion": "2021-06-01"
	}
}

{
	"kind": "EntityRecognitionResults",
	"results": {
		"documents": [{
			"id": "1",
			"entities": [{
				"text": "trip",
				"type": "Event",
				"offset": 18,
				"length": 4,
				"score": 0.74,
				"tags": ["Event"]
			}, {
				"text": "Seattle",
				"type": "Location",
				"offset": 26,
				"length": 7,
				"score": 1.0,
				"tags": ["Location", "GPE", "City"]
			}, {
				"text": "last week",
				"type": "Temporal",
				"offset": 34,
				"length": 9,
				"score": 0.8,
				"tags": ["Temporal", "DateRange"],
				"metadata": {
					"begin": "2022-01-03 00:00:00",
					"end": "2022-01-10 00:00:00"
				}
			}],
			"warnings": []
		}],
		"errors": [],
		"modelVersion": "2023-04-01"
	}
}