Question 1 of 30
In a customer service chatbot application, the goal is to enhance user experience by accurately understanding and responding to customer inquiries. The chatbot utilizes Natural Language Processing (NLP) techniques to analyze user input. If the chatbot is designed to classify user intents and extract relevant entities from the input, which of the following approaches would best optimize its performance in understanding context and nuances in language?
Implementing a transformer-based model like BERT for intent classification and named entity recognition (NER)
Using a simple rule-based system for intent detection and keyword extraction
Relying solely on a bag-of-words model for text representation
Employing a traditional machine learning algorithm without any pre-trained embeddings

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