D is the answer.
https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/named-entity-recognition/overview
Named Entity Recognition (NER) is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities.
https://learn.microsoft.com/en-us/training/modules/analyze-text-with-text-analytics-service/2-get-started-azure
- Entity recognition
You can provide the Language service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
wrong answer. Entity recognition is specifically designed to identify and extract named entities, which can include various types of information such as names of people, organizations, locations (cities, countries), dates, and more.
The correct answer is D. entity recognition.
Entity recognition is a natural language processing (NLP) task that involves identifying and extracting named entities from text. Named entities are typically people, places, organizations, dates, and times. In this case, the solution is extracting the mentions of city names, which are places.
The other options are not correct. Speech recognition is the process of converting spoken language into text. Sentiment analysis is the process of identifying the sentiment of a text, such as whether it is positive, negative, or neutral. Key phrase extraction is the process of identifying the most important words or phrases in a text.
I agree with the comments-
Entity Recognition 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.
I would say: D
Overall, entity recognition is the more appropriate technique for analyzing social media posts to extract mentions of city names and the city names discussed most frequently, as it is specifically designed for identifying and classifying real-world entities within text.
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