## 4 Discussion  
When analyzing the results obtained throughout this work, the profound influence of specific characteristics on the performance of a chatbot based on NLP is evident. The intersection between traditional machine learning metrics and the peculiarities of natural language processing has provided unique insight into how chatbots interpret and respond to user queries. When analyzing the results obtained throughout this work, the profound influence of specific characteristics on the performance of a chatbot based on NLP is evident. The intersection between traditional machine learning metrics and the peculiarities of natural language processing has provided unique insight into how chatbots interpret and respond to user queries. As highlighted in the study by [PERSON] et al. (2020), advanced techniques, such as visual semantic embedding regularization with contrastive learning, are critical to improving accuracy in natural language processing and human-computer interactions. This approach is particularly relevant to our discussion of the importance of syntax in chatbot training, as it highlights how the understanding and interpretation of requests can be optimized using sophisticated machine learning techniques. Recent studies indicate that chatbots trained with an emphasis on syntax show a more remarkable ability to distinguish between different types of requests, thus improving the relevance and accuracy of their responses. Case studies, especially in help desk environments, show \begin{table}
\begin{tabular}{c c c} \hline \hline
**NLP Tool** & **Complexity of the Question** & **Response Accuracy (\%)** \\ \hline BERT & Simple & 98 \\ BERT & Moderate & 88 \\ BERT & Complex & 72 \\ GPT & Simple & 96 \\ GPT & Moderate & 85 \\ GPT & Complex & 78 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Chatbot response accuracy comparison Table: evaluation with BERT and GPT. that chatbots with advanced syntactic interpretation skills can answer complex technical questions more accurately, reducing the need for human intervention and increasing customer satisfaction. This analysis highlights the importance of investing in developing natural language processing capabilities to optimize human-computer interactions. Syntactic structure, for example, emerged as a dominant feature, reflecting the fundamental intuition that how a sentence is structured influences the interpretation of its meaning. These findings align with previous research emphasizing the importance of grammatical structure in NLP [38]. In reviewed work, it has been observed that models trained with a deep understanding of grammar tend to outperform those based solely on critical terms or direct matching approaches. On the other hand, the relevance of behavioral history highlights NLP's evolution towards more contextual systems [39]. A chatbot that can remember past interactions and adjust its responses accordingly is undoubtedly more effective. This trend has been reflected in contemporary research highlighting the need for more personalized and adaptive recommender systems. The findings of this study align with emerging trends in the field of NLP, which seek to develop more adaptable and personalized chatbot models. The ability to recalibrate and retrain the model based on the specific demands of the application domain is a feature that is becoming increasingly relevant in NLP research. As for demographic data, its importance may initially seem minor compared to more direct linguistic characteristics. However, when considering the chatbot in a real-world scenario, these data can be essential to adjust the tone, style, and content of the response, especially in business applications where knowing the customer is crucial [40]. When contrasting our results with previous work, a clear trend is observed towards more holistic and contextual models in the field of NLP. Where rules-based or simple term-matching approaches once predominated, we now see a growing appreciation for context, history, and personalization [22; 41].