AI-900 Question 192
Select 3A company wants to implement a chatbot for customer support that can understand user queries in multiple languages and provide appropriate responses. Which NLP features would be most relevant to this scenario?
- A
Language detection
- B
Sentiment analysis
- C
Entity recognition
- D
Translation
- E
Key phrase extraction
Show answer and explanation
Correct answers: A, C, D
Explanation
To build a chatbot capable of understanding and responding to multilingual user queries, features like language detection, entity recognition, and translation are critical. Language detection allows the chatbot to determine the language of the incoming query. Translation ensures seamless interaction across languages, while entity recognition helps extract key details from the query to provide relevant responses. Sentiment analysis and key phrase extraction, while useful in other scenarios, are not core functionalities for this use case.
- A. Correct.
Language detection is important to identify the language of the user's query so the chatbot can process it appropriately or route it to the correct translation or response logic.
- B. Incorrect.
Sentiment analysis, while useful in understanding user emotions, is not directly relevant to fulfilling the chatbot's ability to understand and respond to multilingual queries.
- C. Correct.
Entity recognition is crucial for identifying specific pieces of information (e.g., names, dates, or product names) within the user's query, which helps the chatbot provide accurate responses.
- D. Correct.
Translation is essential for converting user queries from one language to another if the chatbot's core functionality or training is based on a specific language.
- E. Incorrect.
Key phrase extraction focuses on identifying important phrases in a text but is not a primary feature for understanding and responding to multilingual queries.