## 2 Materials and Methods
### Syntactic Elements in Chatbots  
Chatbots face challenges with complex and ambiguous syntactic structures, which begs the discussion of how technologies such as transformer-based language models have enabled a better understanding of these complexities. For example, models like BERT analyze the full context of words in a sentence, significantly improving the chatbot's understanding.  
Syntax, an essential aspect of NLP, determines how sentences are structured in human language. Chatbots use syntactic rules to identify subjects, verbs, and objects in a sentence, allowing them to understand the user's intentions. For example, in the sentence \"Can you show me the weather today?\", an efficient chatbot identifies \"show me the weather\" as the main action and \"today\" as the weather, allowing for an accurate and contextual response. A chatbot with advanced syntactic skills can discern between a direct and an indirect request. For example, the \"I need a taxi\" request is direct and requires immediate action, while \"I wonder how I will get home\" is indirect and might require a more informative or advisory response.  
In some cases, chatbots with advanced syntactic capabilities have correctly interpreted the tone and intent of requests, provided more appropriate responses, and improved the user experience. These highlight that, by understanding not only the literal content but also the implicit context, chatbots can offer more natural and efficient interactions, which underlines the importance of advanced syntactic skills in chatbots, directly linking them to satisfaction and effectiveness in communicating with users.  
An example is a chatbot implemented by a telecommunications company to handle customer queries. The chatbot, equipped with syntactic processing, handles various requests, from simple questions like \"What is my balance?\" to more complex queries like \"I'm having problems with my internet connection; could you help me?\"  
The chatbot identifies the required action to balance the simple question. However, in the complex query, the chatbot not only recognizes the support request but also picks up the implied urgency and responds with a series of troubleshooting steps tailored to the possible cause of the problem. The chatbot's ability to understand and respond to these varied requests demonstrates its effectiveness in interpreting user intent. Customers report high satisfaction levels as the chatbot provides quick and accurate responses, reducing the need for human interaction for common problems and improving customer service efficiency. In this case, a chatbot with advanced syntactic skills can significantly improve the customer experience, highlighting the importance of this technology in interactive and automated customer service.  
The model uses the user's behavioral history to provide consistent and contextually relevant responses. As the user interacts with the chatbot, the history is continually updated, allowing the model to better understand the user's needs and preferences.  
Integrating behavioral history into chatbot responses is a crucial process to improve the quality of interactions. When the chatbot receives a new query, it analyzes behavioral history to identify patterns, recurring themes, and user preferences. This allows the model to generate responses that are aligned with previous interactions. Behavior history personalizes the chatbot's responses and provides appropriate context. For example, if a user has asked queries related to technology products in the past, the chatbot can adapt its tone and content to be relevant to this specific interest.  
In addition to personalization, integrating behavioral history helps the chatbot retain context during a conversation. The chatbot can remember what was discussed and build on that information in subsequent responses. Ultimately, behavioral history integration aims to improve the user experience by making interactions more seamless, relevant, and personalized. This contributes to greater user satisfaction and chatbot effectiveness.