You are developing a solution that uses the Text Analytics service.
You need to identify the main talking points in a collection of documents.
Which type of natural language processing should you use?
Click on the arrows to vote for the correct answer
A. B. C. D.B
Broad entity extraction: Identify important concepts in text, including key
Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processingThe task of identifying the main talking points in a collection of documents requires extracting the most important and relevant phrases or keywords. Therefore, the type of natural language processing that should be used is key phrase extraction.
Key phrase extraction is a technique used in natural language processing (NLP) to automatically identify the most important words and phrases in a text. The goal of this technique is to extract a summary or representation of the most relevant and meaningful information contained in a document or set of documents. Key phrase extraction can be used to improve search results, to identify important topics in a collection of documents, or to categorize documents based on their content.
In the context of the Microsoft Azure Text Analytics service, key phrase extraction is a feature that allows users to extract the most relevant and meaningful phrases from a text document. The Text Analytics service uses machine learning algorithms to analyze the text and identify the key phrases that best represent the content of the document.
In summary, the correct answer to the question is B. Key phrase extraction is the type of natural language processing that should be used to identify the main talking points in a collection of documents.