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named-entity-recognition API

Introduction

The named-entity-recognition API enables you to locate and categorize named entities in text into pre-defined categories such as person and organization using Amazon Mechanical Turk (MTurk). This is useful in the field of natural language processing, for example when extracting information from machine-readable documents.

The API takes as input of a string of text to be analyzed, max length 1,600 characters, and returns an array of named entities indicating where they are located in the input string starting at index 0 and the type of entity of type Person, Location, Organization, Event, Date, or Brand. When you call this API, it uses your MTurk Requester account and AWS account to automatically create HITs for Workers on MTurk to complete, and automatically processes answers from Workers and returns the result.

Example Input:

{
  "input": {
    "text": "The Grammys nominated The Chainsmokers for Best New
		Artist for simply repeating \"We ain’t ever gettin older\""
   }
}

Example Result:

{
  "result": {"coreferences": {
      ["entities": [
		{"span":{"endIndex": 11, "startIndex": 4, 
			"text": "Grammys", 'type’: 'event'}}, 
		{"span":{"endIndex": 38, "startIndex": 22, 
			"text": "The Chainsmokers", 'type’: 'organization' }}, 
      ] 
    ] }
  }
}

Here’s how the input might be presented in a HIT to Workers on MTurk.

Worker Preview of HIT

All of the single purpose APIs follow the same REST pattern as described in our REST API reference. They require a function name indicating which API you want to call, and a unique Task name used to represent your input and the corresponding result.

named-entity-recognition-test is a version of the named-entity-recognition API meant for testing your integration. It has the same interface as the production version, but Tasks always complete quickly with mock results. HITs are not created on MTurk so you don’t need to pay Workers for rewards.

Note: This will return a random mock result when you call get_task with named-entity-recognition-test

The rest of this article provides instructions on using the named-entity-recognition API using the MTurk Python Client. Download Sample Python code for named-entity-recognition-test

Create a Task

This Python sample calls the named-entity-recognition API using Boto3 and the MTurk Python client.

Tasks are created by calling put_task. For more details for how to use put_task, check out Instructions for creating your first Task for a MTurk single purpose API.

The body of a put_task request looks like this:

{
  "input": {
    "text": <text you want analyzed>
  }
}
Input structure
Name Description Type Required
"text" text you want analyzed String (between 1-1,600 characters) Yes

Example request

text = 'The Grammys nominated The Chainsmokers for Best New 
		Artist for simply repeating "We ain\'t ever gettin older"'

put_result = crowd_client.put_task('named-entity-recognition', 'my-task-name',                         
                             {'text': text})

Example response

{
  "taskName": "my-task-name",
  "input": {
    "text": "The Grammys nominated The Chainsmokers for Best New 
		Artist for simply repeating \"We ain’t ever gettin older\""
  },
  "problemDetails": null,
  "state": "processing",
  "result": null
}

Get a Result

This Python sample calls the named-entity-recognition API using Boto3 and the MTurk Python client.

After creating a Task, users can call the get_task to poll its current state.

Remember it can take some time for a Worker to complete a Task, so you might have to wait before getting a result.

For more details for how to use get_task, check out Instructions for creating your first Task for a MTurk single purpose API.

Example request

client.get_task('named-entity-recognition', 'my-task-name')

Response structure:

Name Description Type
"taskName" user-provided Task name String
"input" input provided by user when Task was created JSON Object
"problemDetails" if the "state" is "failed" - details about a failed Task JSON Object or null
"state" current Task state - one of "processing", "completed" or "failed" String
"result" if the "state" is "completed" - the results of the completed Task JSON Object or null

Result structure:

Name Value Type
"entities" list of named entities in provided string Array of JSON Objects
"span" an extracted entity, along with its position in the input string g JSON Object
"endIndex" position of the end of selected entity Integer
"startIndex" position of the start of selected entity Integer
"text" text of selected entity String
"type" one of "person", "location", "organization", "event", "date", or "brand". String

Example response

Example response for a successful Task:

{
  "taskName": "my-task-name",
  "input": {
    "text": "The Grammys nominated The Chainsmokers for Best New 
		Artist for simply repeating \"We ain’t ever gettin older\""
  },
  "problemDetails": null,
  "state": "completed",
  {
  "result": {"coreferences": {
      ["entities": [
		{"span":{"endIndex": 11, "startIndex": 4, 
			"text": "Grammys", 'type’: 'event'}}, 
		{"span":{"endIndex": 38, "startIndex": 22, 
			"text": "The Chainsmokers", 'type’: 'organization' }}, 
      ] 
    ] }
  }
}

Example response for a failed Task:

{
  "taskName": "my-task-name",
  "input": {
    "text": "The Grammys nominated The Chainsmokers for Best New 
		Artist for simply repeating \"We ain’t ever gettin older\""
  },
  "problemDetails": {
    "code": "Expired",
    "message": "Not enough Workers provided answers for your task within the allotted time."
  },
  "state": "failed",
  "result": null
}

Note: If Workers do not find any entities in the provided input string, "state" will still return as complete, but in the result, "entities" will be an empty array.

Delete Task when Task is complete

This Python sample calls the named-entity-recognition API using Boto3 and the MTurk Python client.

A user can optionally delete a finished Task (one whose state is either “completed” or “failed”). After you delete a Task, you can reuse the Task name for a future Task.

Note: Deleting a Task that is still being processed (i.e., whose state is “processing”) is not allowed.

For more details for how to use delete_task, check out Instructions for creating your first Task for a MTurk single purpose API.

Example Input

client.delete_task('named-entity-recognition', 'my-task-name')

Example Output

A successful request for the delete_task operation returns with no errors and an empty body.

Get Started with named-entity-recognition

  1. Set up your Amazon Mechanical Turk (MTurk) Requester account and AWS account
  2. Set up permissions to call an MTurk single purpose API
  3. Instructions for creating your first Task for a MTurk single purpose API

MTurk has released several APIs for common use cases, click here to see a list of all the available APIs.

If you have any questions or feedback, such as methods you wish our client supported, please contact our product team, or submit a pull request on GitHub adding additional functionality to our client.