Which API Helps to Analyze the Text and Send the Tokens With Offset Value and Data Type?


Heather Bennett

In the world of natural language processing (NLP), analyzing text and extracting meaningful information from it is a common task. One crucial step in this process is tokenization, where text is divided into individual units, such as words or sentences, for further analysis.

There are several APIs available that can help with text analysis and provide tokenization features. One such API is the Text Analytics API provided by Microsoft Azure Cognitive Services. This powerful API allows you to analyze text for sentiment analysis, key phrase extraction, language detection, and more.

When it comes to tokenization, the Text Analytics API provides a convenient method called Analyze Text with Offset. This method not only returns the tokens but also provides additional information about each token, including its offset value and data type.

Analyze Text with Offset

The Analyze Text with Offset method in the Text Analytics API takes a text input and returns a JSON response containing an array of tokens along with their respective offset values and data types. This allows you to precisely identify the position of each token within the original text.

To use this method, you need to make an HTTP POST request to the API endpoint with your subscription key and specify the desired features for analysis. Here’s an example using Python:

import requests
import json

# Set up the request headers
headers = {
    'Content-Type': 'application/json',
    'Ocp-Apim-Subscription-Key': 'YOUR_SUBSCRIPTION_KEY'

# Define the request body
data = {
    'documents': [
            'id': '1',
            'text': 'This is an example sentence.'

# Make the HTTP POST request
response = requests.post('https://your-resource-name.cognitiveservices.azure.com/text/analytics/v3.0-preview.1/entities/recognition/general', headers=headers, json=data)

# Parse the response JSON
result = json.loads(response.text)

# Extract the tokens with offset values and data types
tokens = result['documents'][0]['entities']['text']

# Print the tokens
for token in tokens:
    print('Token:', token['text'])
    print('Offset:', token['offset'])
    print('Data Type:', token['type'])

Understanding the Response

The response from the Analyze Text with Offset method contains an array of documents, each representing a text input. In our example, we have only one document with ID ‘1’ and the text ‘This is an example sentence.’

Within each document, there is an ‘entities’ object that contains information about recognized entities in the text. The ‘text’ property within this object represents individual tokens.

For each token, you can access its value using ‘token[‘text’]’, its offset within the original text using ‘token[‘offset’]’, and its data type using ‘token[‘type’]’. This information can be useful for various NLP tasks, such as named entity recognition or part-of-speech tagging.

In Conclusion

The Text Analytics API provides a powerful solution for analyzing text and extracting tokens with offset values and data types. By incorporating this API into your NLP workflows, you can enhance your text analysis capabilities and gain valuable insights from textual data.

Note: Don’t forget to replace ‘YOUR_SUBSCRIPTION_KEY’ and ‘your-resource-name‘ in the code snippet with your actual subscription key and Azure resource name.

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