Original scientific paper

https://doi.org/10.31803/tg-20250410040449

Real-Time Hybrid Query Transformation Method for Enhancing Search System Performance in Korean Language Applications

Hyun Jung Kim orcid.org/0000-0003-3845-0560 ; Sang-Huh College and the Graduate School of Information & Communication, Department of Convergence Information Technology (Artificial Intelligence Major), Konkuk University, 120 Neungdong-ro, Gwangjin-gu, 05029 Seoul, Korea
Sang Hyun Yoo orcid.org/0009-0008-9199-8238 ; School of Computer Science & Engineering, College of IT, Soongsil University, 50 Sadang-ro, Dongjak-gu, Seoul 07027, Korea *

* Corresponding author.

Abstract

This study addresses the real-time performance limits of Korean-language search systems caused by morphological complexity and the cost of semantic processing. We propose a hybrid query transformation method that couples rule-based preprocessing with a Transformer-based postprocessor. The rule-based stage simplifies agglutinative input, and the Transformer refines user intent and semantic context. On a curated Korean query set, our approach attains 89.0% Precision@5 (95% CI: 87.2–90.7) with 95 ms average latency (95% CI: 92–98), about 21% faster than an NLP-only baseline. User surveys and expert interviews further confirm practical applicability. To strengthen reliability and scope transparency, we report five-fold cross-validation, noise-robustness tests (spacing errors, minor typos), and comparisons against open proxy baselines (e.g., BM25+ KoNLPy). These additions clarify the study’s focus on Korean while providing reproducible evidence of robustness, positioning the framework as deployment-ready for Korean and a solid basis for future multilingual extensions.

Keywords

NLP-based postprocessing; query optimization; real-time applications; real-time hybrid query transformation; rule-based preprocessing; search system performance

Hrčak ID:

344762

URI

https://hrcak.srce.hr/344762

Publication date:

13.3.2026.

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