Explain how natural language processing (NLP) can be effectively integrated into a WhatsApp chatbot to handle ambiguous user queries and improve response accuracy.

Natural Language Processing (NLP) significantly enhances a WhatsApp chatbot's ability to understand and respond accurately to ambiguous user queries. 'Natural Language Processing (NLP)' is a field of computer science that enables computers to understand, interpret, and generate human language. NLP allows the chatbot to go beyond simple keyword matching and understand the user's intent, even when the query is not phrased perfectly or contains multiple possible interpretations. One way NLP achieves this is through intent recognition. NLP models can identify the underlying intent of a user's message, even if the user's wording is vague or indirect. For example, if a user types 'I need help with my order,' the NLP model can recognize the intent as 'order support,' even though the user didn't explicitly mention the word 'support.' Another technique is entity extraction. NLP can extract relevant entities (e.g., product names, dates, locations) from the user's message, which helps the chatbot to understand the context of the query. If a user asks 'When will my package arrive?', the NLP model can extract the entity 'package' and associate it with the user's order history. Additionally, NLP enables sentiment analysis. NLP can analyze the sentiment (e.g., positive, negative, neutral) expressed in the user's message, allowing the chatbot to tailor its response accordingly. If a user expresses frustration, the chatbot can offer more empathetic and helpful support. Finally, NLP facilitates disambiguation. When a user's query is ambiguous, NLP can use context and dialogue history to determine the most likely meaning. The chatbot might ask clarifying questions to narrow down the user's intent. For example, if a user asks 'Do you have it?', the chatbot might ask 'Are you referring to a specific product?'. The goal is to create a system that is better than just keyword matching. 'Ambiguous queries' are requests that are not clear or have multiple interpretations.