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You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following exhibit. Which type of natural languages processing was performed?
Suggested Answer:A🗳️
Named Entity Recognition (NER) is the ability to identify different entities in text and categorize them into pre-defined classes or types such as: person, location, event, product, and organization. In this question, the square brackets indicate the entities such as DateTime, PersonType, Skill. Reference: https://docs.microsoft.com/en-in/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-linking?tabs=version-3-preview
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Extract a broad range of pre-built entities such as people, places, organizations, date/time, numerals and over 100 types of personally identifiable information (PII), including protected health information (PHI), in documents using named entity recognition.
Quickly evaluate and identify the main points in unstructured text. Get a list of relevant phrases that best describe the subject of each record using key phrase extraction. Easily organize information to make sense of important topics and trends.
Entity recognition has category classification, key phrase extraction does not. Take a look at Key Phrase extraction results: [https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-keyword-extraction] with NER results: [https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-linking?tabs=version-3-preview]
What is shown in this example of Entity Recognition, not Named Entity Recognition.
Entity recognition (ER) is distinct from NER (Named Entity Recognition). NER focuses only on proper nouns, while ER looks at identify all types of nouns
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While both entity recognition and key phrase extraction involve extracting information from text, entity recognition specifically targets named entities (e.g., people, organizations, locations), while key phrase extraction focuses on identifying the most significant phrases or terms that represent the main topics or concepts within the text
A. Entity recognition
In the link provided for the answer you can see the following example:
NAMED ENTITIES: Contoso [Organization]
Steakhouse [Location]
NYC [Location-GPE]
last week [DateTime-DateRange]
dinner party [Event]
chief cook [PersonType]
owner [PersonType]
John Doe [Person]
kitchen [Location-Structural]
Sirloin steak [Product]
www.contososteakhouse.com [URL]
312-555-0176 [Phone Number]
email [Skill]
[email protected] [Email]
contososteakhouse [Organization]
So correct link but wrong answer :)
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