George Pashev, Silvia Gaftandzhieva
University of Plovdiv Paisii Hilendarski, Plovdiv (Bulgaria)
https://doi.org/10.53656/math2026-1-4-lhc
Abstract. Universities show a rapidly growing interest in using Artificial Intelligence tools. This paper presents LexaQuery, a new chatbot system for university information services that efficiently handles university information inquiries by combining computational linguistics with SQL database querying. Instead of relying on large, resource-intensive large language models (LLMs), LexaQuery uses rule-based natural language processing to translate student questions into structured SQL. The system has a three-tier architecture comprising language processing for query translation, knowledge extraction (from databases and web scraping), and a user interaction layer. Performance evaluation at the University of Plovdiv Paisii Hilendarski demonstrates that this hybrid approach provides significantly faster response times than neural network-based alternatives while maintaining satisfactory accuracy for domain-specific tasks. The paper discusses the system’s advantages in terms of integration with existing university information systems, performance efficiency, explainability, and the ability to operate without extensive computational resources, as well as its linguistic flexibility and limitations in domain adaptation. This research contributes to developing practical, efficient chatbot systems for educational institutions with constrained technical infrastructure.
Keywords: computational linguistics, SQL generation, chatbot systems, natural language processing
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