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AI-900: Efficient Insurance Claim Processing: Leveraging Natural Language Processing for Key Term Extraction

Discover the ideal AI workload for extracting key terms from insurance claim reports and generating summaries. Explore the benefits and capabilities of conversational AI, anomaly detection, natural language processing, and computer vision. Streamline your insurance claim processing by harnessing the power of natural language processing to extract crucial information and enhance efficiency.


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 Al workload should you use?

A. conversational Al
B. anomaly detection
C. natural language processing
D. computer vision


C. natural language processing


The correct answer is C. natural language processing.

Natural language processing (NLP) is a type of Al workload that deals with analyzing, understanding, and generating natural language text or speech. NLP can be used for various tasks such as sentiment analysis, text summarization, machine translation, named entity recognition, question answering, and more.

In this scenario, you need to extract key terms from the insurance claim reports that are stored as text and generate summaries. This is an example of text summarization, which is a subtask of NLP. Text summarization aims to produce a concise and coherent summary of a longer text document by extracting the most important information and discarding the irrelevant details. Text summarization can be done in two ways: extractive or abstractive. Extractive summarization selects the most salient sentences or phrases from the original text and concatenates them to form a summary. Abstractive summarization generates new sentences or phrases that capture the main idea of the original text.

Therefore, to perform text summarization on the insurance claim reports, you should use natural language processing as the type of Al workload.

Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s). Key phase extraction is a part of Text Analytics. The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform advanced natural language processing over raw text.


Microsoft Docs > Microsoft Azure AI Fundamentals: Explore natural language processing > Analyze text with the Language service > Get started with text analysis

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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