Key phrases in a document denote the essence of the content. They provide an overview of the content of the document without readers diving deep into it. In the content-rich world today, automated key phrase extraction offers an efficient solution for organizing, processing, and understanding various types of data.
Microsoft Azure AI provides a suite of cognitive services that aid in key phrase extraction using pre-built AI models. The Text Analytics API is a cloud-based service that provides advanced natural language processing techniques over raw text, one of them being key phrase extraction.
1. Understanding Text Analytics API for Key Phrase Extraction
Text Analytics API is a part of Azure’s Cognitive Services that extracts key phrases from a document to understand the primary talking points. This is particularly helpful with large unstructured documents as it surfaces the main points.
The key phrase extraction feature returns a list of strings denoting the key talking points in the input text. For example, given the input text “The food at the restaurant was great. We enjoyed pasta and a chocolate dessert”, instead of returning a full summary, it highlights the main points like “food,” “restaurant,” “pasta,” and “chocolate dessert”.
2. How to Retrieve and Process Key Phrases
To retrieve and process key phrases from a text document, use the Azure.TextAnalytics library in Python.
Firstly, create a client and authenticate it with an endpoint and key acquired from your Azure resources.
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential
def authenticate_client():
ta_credential = AzureKeyCredential("
text_analytics_client = TextAnalyticsClient(
endpoint="
credential=ta_credential)
return text_analytics_client
Next, use the client to extract key phrases from the text document.
def key_phrase_extraction_example(client):
try:
document = ["The food at the restaurant was great. We enjoyed pasta and a chocolate dessert"]
response = client.extract_key_phrases(document)
for result in response:
print("Key phrases:")
print(result.key_phrases)
except Exception as err:
print("Encountered exception: {}".format(err))
This will return a list of key phrases present in the document – the main points of interest.
3. Comparison: Text Analytics API vs. Manual Extraction
Manually extracting key phrases from large amounts of data is labor-intensive and could cause bias due to human error or subjectivity. On the other hand, Azure’s Text Analytics API enables automated, efficient, and high-volume processing. It not only reduces the labor and time investment but also provides consistent, unbiased results.
| Text Analytics API | Manual Extraction | |
| Efficiency | High | Low |
| Volume | High | Low |
| Consistency | Yes | No |
| Unbiased | Yes | No |
By implementing Azure’s Text Analytics API in your solutions, you can unlock the potential of unstructured data with greater efficiency and consistency. The API’s key phrase extraction feature is a powerful tool which holds the capability of transforming how we process and understand large volumes of text data. By doing so, it brings us a step closer to harnessing the full potential of AI in data understanding and processing.
Practice Test
True/False: Azure’s Text Analytics API has a feature to retrieve key phrases from texts.
- True
- False
Answer: True
Explanation: Azure’s Text Analytics API has a Key Phrase Extraction feature that enables developers to extract key talking points and entities from text formats.
Which Azure service provides key phrases extraction functionalities?
- a) Computer Vision
- b) Text Analytics
- c) Speech Service
- d) Video Indexer
Answer: b) Text Analytics
Explanation: The Azure’s Text Analytics API is specifically designed to provide key phrases extraction, sentiment analysis, and other text-related insights from unstructured data.
True/False: Key phrases extraction can help in getting insights from unstructured text data.
- True
- False
Answer: True
Explanation: Key phrases extraction is a text analytics process which identifies the key topics discussed in a text, providing insightful analysis of unstructured data.
What is the Text Analytics API used for?
- a) Extracting key phrases
- b) Detecting sentiment
- c) Recognizing entities
- d) All of the above
Answer: d) All of the above
Explanation: The Text Analytics API in Azure is a cloud-based service providing advanced natural language processing features including key phrase extraction, sentiment detection, and entity recognition.
True/False: Key-phrase extraction does not support multiple languages.
- True
- False
Answer: False
Explanation: Azure’s Text Analytics API supports multiple languages for key-phrase extraction, providing broader accessibility and functionality.
Can key phrase extraction be used for analyzing customer reviews?
- a) Yes
- b) No
Answer: a) Yes
Explanation: Key phrase extraction can be effectively used to identify main points or themes in customer reviews, providing valuable insights for business enhancements.
True/False: Key phrases extraction can be used to categorize content based on the extracted phrases.
- True
- False
Answer: True
Explanation: The key phrases extracted from content can indeed be used to categorize or tag the content, which is especially useful in content management, search engines, etc.
Which Azure service is best suited for processing spoken language?
- a) Text Analytics
- b) Computer Vision
- c) Bing Search
- d) Speech Service
Answer: d) Speech Service
Explanation: The Azure Speech Service is specifically tailored for processing and transcribing spoken language into written text.
True/False: The output of key phrase extraction is a list of string values.
- True
- False
Answer: True
Explanation: Key phrase extraction identifies the main talking points in the input text and returns these as a list of string values representing the key phrases.
What is the primary requirement for processing key phrases using Azure’s Text Analytics?
- a) Data in CSV format
- b) Data in audio format
- c) Data in text format
- d) Data in video format
Answer: c) Data in text format
Explanation: Azure’s Text Analytics operates predominantly on textual data. Therefore, it requires the data to be in a textual format to process it and extract key phrases.
True/False: Retrieving and processing key phrases is a form of text mining.
- True
- False
Answer: True
Explanation: Key phrase extraction is indeed a form of text mining as it derives high-quality information from text by drawing upon computational algorithms.
What are the input languages supported by Azure’s Text Analytics for key phrase extraction?
- a) Only English
- b) Only French
- c) English and Spanish
- d) Multiple languages
Answer: d) Multiple languages
Explanation: Azure’s Text Analytics API supports multiple languages for key phrase extraction, including but not limited to English, French, Spanish, and German.
Interview Questions
What is key phrase extraction in Azure AI?
Key phrase extraction is a feature of Text Analytics in Microsoft’s Azure AI that uses machine learning to identify the essential topics or phrases within a piece of text. This helps users understand the primary points of the content without needing to read through the entire text.
How can you initiate the extraction of key phrases using Azure Text Analytics?
Key phrases extraction in Azure can be initiated through a REST API call. This can be done using python, C#, JAVA, and more.
How are key phrases returned in Azure Text Analytics?
Key phrases are generally returned as a list of strings, with each string representing a recognized key phrase.
What languages are currently supported by Azure Text Analytics for key phrase extraction?
As of now, Azure Text Analytics supports English, German, Spanish, Japanese, Chinese, Korean, French, Italian, and Dutch for key phrase extraction.
What kinds of applications are Azure Text Analytics’ key phrase extraction intended for?
Key phrase extraction is often used to analyze and get insights from customer feedback, analyze social media content, understand user intent, spot trends in large sets of content and much more.
What is the maximum number of documents that can be sent in one key phrases extraction request with Azure Text Analytics API?
A maximum of 5,000 documents can be sent in one request for the service.
Can Azure Text Analytics API extract key phrases from documents in different languages at the same time?
Yes, but the language code of each document needs to be specified in the request.
Can Azure Text Analytics process longer documents for key phrase extraction?
Each Text Analytics API call has a size limit of 5120 characters (using coding units in the Unicode standard). For longer documents, client-side text segmentation will need to be carried out before sending the request.
How does Azure Text Analytics handle improper input or unsupported language for key phrase extraction?
Azure Text Analytics will return an error for that specific document with an error message that specifies the reason for the failure.
Can you customize the model used for key phrase extraction in Azure Text Analytics?
As of now, the model used for key phrase extraction is not customizable and is managed by Azure.
Is there a way to perform sentiment analysis in conjunction with key phrase extraction in Azure Text Analytics?
Yes, Azure Text Analytics provides a Sentiment analysis API which can be used in conjunction with key phrase extraction to provide additional context to the detected key phrases.
Can you extract key phrases from real-time data feeds in Azure?
While real-time extraction is not directly supported in Azure Text Analytics, pipeline architectures can be designed to continuously feed input data to the Text Analytics API, thereby achieving near-real-time key phrase extraction.
Does Azure Text Analytics support batch processing for key phrase extraction?
Yes. The Azure Text Analytics API does support batch processing. You can submit a collection of documents in a single request for key phrase extraction.
How fast can Azure Text Analytics API process key phrases extraction?
Azure Text Analytics is designed to handle high volume of texts and can process thousands of documents per second for key phrase extraction.
Are there any statistical requirements for the text used in key phrase extraction using Azure Text Analytics API?
There’s no specific statistical requirement. However, to get meaningful results, the text should be at least one sentence long and consist of identifiable key phrases.
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