AI-900 Question 191
Select 3A company wants to develop a customer service chatbot that can understand and respond to customer inquiries in natural language. Which of the following features of Natural Language Processing (NLP) workloads are most relevant to this scenario?
- A
Text classification
- B
Named entity recognition
- C
Sentiment analysis
- D
Text-to-Speech conversion
- E
Language understanding
Show answer and explanation
Correct answers: A, B, E
Explanation
Building a chatbot for customer service involves several core NLP features. Text classification helps categorize queries, named entity recognition identifies key information within the user's input, and language understanding ensures the chatbot can interpret the meaning and intent behind natural language queries. These are essential components for creating an effective conversational AI solution.
- A. Correct.
Text classification is relevant because the chatbot needs to classify the customer's inquiry into specific categories (e.g., billing, technical support) to provide an appropriate response.
- B. Correct.
Named entity recognition is critical as the chatbot needs to identify specific entities such as names, dates, or product names within the customer's query to tailor its response.
- C. Incorrect.
Sentiment analysis is less relevant in this scenario because understanding the emotional tone of the customer's message might not directly contribute to responding to their inquiry.
- D. Incorrect.
Text-to-Speech conversion is unrelated to the understanding of natural language. While it could be used to generate spoken responses, it is not directly relevant to the core NLP tasks for understanding the customer's query.
- E. Correct.
Language understanding is highly relevant as the chatbot must comprehend the intent and meaning of the customer's natural language input to provide accurate and contextually appropriate responses.