You have insurance claim reports that are stored as text.
You need to extract key terms from the reports to generate summaries.
Which type of AI workload should you use?
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A. B. C. D.A
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processingThe correct answer is A. natural language processing (NLP).
Natural language processing is a type of AI workload that involves the use of algorithms to analyze, understand, and generate human language. In this scenario, the goal is to extract key terms from text documents to generate summaries. NLP can help achieve this by using techniques such as part-of-speech tagging, entity recognition, and sentiment analysis to identify relevant terms and phrases.
Conversational AI, on the other hand, involves the development of chatbots and virtual assistants that can communicate with humans in natural language. This workload is not directly relevant to the task of extracting key terms from text documents.
Anomaly detection is a type of AI workload that involves identifying patterns in data that deviate from normal behavior. While it could be used to detect unusual patterns in insurance claims data, it is not directly relevant to the task of extracting key terms from text documents.
Computer vision involves the use of AI to interpret and understand visual data, such as images and videos. This workload is not directly relevant to the task of processing text data.
Therefore, the most appropriate type of AI workload for extracting key terms from text documents in this scenario is natural language processing (NLP).