Named Entity Recognition

To extract information from unstructured text and classify it into predefined categories, use an Amazon SageMaker Ground Truth named entity recognition (NER) labeling task. Traditionally, NER involves sifting through text data to locate noun phrases, called named entities, and categorizing each with a label, such as "person," "organization," or "brand." You can broaden this task to label longer spans of text and categorize those sequences with predefined labels that you specify.

When tasked with a named entity recognition labeling job, workers apply your labels to specific words or phrases within a larger text block. They choose a label, then apply it by using the cursor to highlight the part of the text to which the label applies. The Ground Truth named entity recognition tool supports overlapping annotations, in-context label selection, and multi-label selection for a single highlight. Also, workers can use their keyboards to quickly select labels.

You can create a named entity recognition labeling job using the Ground Truth section of the Amazon SageMaker console or the CreateLabelingJob operation.

Important

If you manually create an input manifest file, use "source" to identify the text that you want labeled. For more information, see Input Data.

Create a Named Entity Recognition Labeling Job (Console)

You can follow the instructions Create a Labeling Job (Console) to learn how to create a named entity recognition labeling job in the SageMaker console. In Step 10, choose Text from the Task category drop down menu, and choose Named entity recognition as the task type.

Ground Truth provides a worker UI similar to the following for labeling tasks. When you create the labeling job with the console, you specify instructions to help workers complete the job and labels that workers can choose from.

Create a Named Entity Recognition Labeling Job (API)

To create a named entity recognition labeling job, using the SageMaker API operation CreateLabelingJob. This API defines this operation for all AWS SDKs. To see a list of language-specific SDKs supported for this operation, review the See Also section of CreateLabelingJob.

Follow the instructions on Create a Labeling Job (API) and do the following while you configure your request:

• Pre-annotation Lambda functions for this task type end with PRE-NamedEntityRecognition. To find the pre-annotation Lambda ARN for your Region, see PreHumanTaskLambdaArn .

• Annotation-consolidation Lambda functions for this task type end with ACS-NamedEntityRecognition. To find the annotation-consolidation Lambda ARN for your Region, see AnnotationConsolidationLambdaArn.

• You must provide the following ARN for HumanTaskUiArn:

  arn:aws:sagemaker:aws-region:394669845002:human-task-ui/NamedEntityRecognition

  Replace aws-region with the AWS Region you use to create the labeling job. For example, use us-west-1 if you create a labeling job in US West (N. California).

• Provide worker instructions in the label category configuration file using the instructions parameter. You can use a string, or HTML markup language in the shortInstruction and fullInstruction fields. For more details, see Provide Worker Instructions in a Label Category Configuration File.

  "instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}

The following is an example of an AWS Python SDK (Boto3) request to create a labeling job in the US East (N. Virginia) Region. All parameters in red should be replaced with your specifications and resources.

response = client.create_labeling_job(
    LabelingJobName='example-ner-labeling-job',
    LabelAttributeName='label',
    InputConfig={
        'DataSource': {
            'S3DataSource': {
                'ManifestS3Uri': 's3://bucket/path/manifest-with-input-data.json'
            }
        },
        'DataAttributes': {
            'ContentClassifiers': [
                'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent',
            ]
        }
    },
    OutputConfig={
        'S3OutputPath': 's3://bucket/path/file-to-store-output-data',
        'KmsKeyId': 'string'
    },
    RoleArn='arn:aws:iam::*:role/*',
    LabelCategoryConfigS3Uri='s3://bucket/path/label-categories.json',
    StoppingConditions={
        'MaxHumanLabeledObjectCount': 123,
        'MaxPercentageOfInputDatasetLabeled': 123
    },
    HumanTaskConfig={
        'WorkteamArn': 'arn:aws:sagemaker:region:*:workteam/private-crowd/*',
        'UiConfig': {
            'HumanTaskUiArn': 'arn:aws:sagemaker:us-east-1:394669845002:human-task-ui/NamedEntityRecognition'
        },
        'PreHumanTaskLambdaArn': 'arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition',
        'TaskKeywords': [
            'Named entity Recognition',
        ],
        'TaskTitle': 'Named entity Recognition task',
        'TaskDescription': 'Apply the labels provided to specific words or phrases within the larger text block.',
        'NumberOfHumanWorkersPerDataObject': 1,
        'TaskTimeLimitInSeconds': 28800,
        'TaskAvailabilityLifetimeInSeconds': 864000,
        'MaxConcurrentTaskCount': 1000,
        'AnnotationConsolidationConfig': {
            'AnnotationConsolidationLambdaArn': 'arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition'
        },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)

Provide Worker Instructions in a Label Category Configuration File

You must provide worker instructions in the label category configuration file you identify with the LabelCategoryConfigS3Uri parameter in CreateLabelingJob. You can use these instructions to provide details about the task you want workers to perform and help them use the tool efficiently.

You provide short and long instructions using shortInstruction and fullInstruction in the instructions parameter, respectively. To learn more about these instruction types, see Creating Instruction Pages.

The following is an example of a label category configuration file with instructions that can be used for a named entity recognition labeling job.

{
  "document-version": "2018-11-28",
  "labels": [
    {
      "label": "label1",
      "shortDisplayName": "L1"
    },
    {
      "label": "label2",
      "shortDisplayName": "L2"
    },
    {
      "label": "label3",
      "shortDisplayName": "L3"
    },
    {
      "label": "label4",
      "shortDisplayName": "L4"
    },
    {
      "label": "label5",
      "shortDisplayName": "L5"
    }
  ],
  "instructions": {
    "shortInstruction": "<p>Enter description of the labels that workers have 
                        to choose from</p><br><p>Add examples to help workers understand the label</p>",
    "fullInstruction": "<ol>
                        <li><strong>Read</strong> the text carefully.</li>
                        <li><strong>Highlight</strong> words, phrases, or sections of the text.</li>
                        <li><strong>Choose</strong> the label that best matches what you have highlighted.</li>
                        <li>To <strong>change</strong> a label, choose highlighted text and select a new label.</li>
                        <li>To <strong>remove</strong> a label from highlighted text, choose the X next to the 
                        abbreviated label name on the highlighted text.</li>
                        <li>You can select all of a previously highlighted text, but not a portion of it.</li>
                        </ol>"
  }
}

Named Entity Recognition Output Data

Once you have created a named entity recognition labeling job, your output data will be located in the Amazon S3 bucket specified in the S3OutputPath parameter when using the API or in the Output dataset location field of the Job overview section of the console.

To learn more about the output manifest file generated by Ground Truth and the file structure the Ground Truth uses to store your output data, see Output Data.