Understanding the Effectiveness of Query Expansion in IndoSBERT-Based Semantic Retrieval - Dela Puspita Lasminingrum; Eva Yulia Puspaningrum; Budi Mukhammad Mulyo (Journal of INISTA, 2026)
The advancement of information retrieval systems has shifted from keyword-based approaches to semantic retrieval using transformer-based models such BERT and its variants. Despite their ability to capture contextual meaning, the vocabulary mismatch problem between queries and documents remains a key challenge. Query expansion (QE) is commonly used to address this issue, but its effectiveness in semantic retrieval is not always consistent. This study aims to analyze the impact of query expansion on a semantic retrieval system based on a fine-tuned IndoSBERT model using a dataset of undergraduate thesis titles and abstracts at repository UPN “Veteran” Jawa Timur. A hybrid QE approach is proposed by combining pretrained FastText and domain-specific Word2Vec embeddings, with and without filtering mechanisms. The system performance is evaluated using Precision@15, Recall@15, Mean Average Precision (MAP), and nDCG@15. The results show that QE can improve retrieval performance when properly controlled. The best performance is achieved by the hybrid QE with filtering, where MAP increases from 0.389 (without QE) to 0.483 and nDCG reaches 0.911. In contrast, FastText-based QE without filtering results in performance degradation due to query drift. It can be concluded that the effectiveness of QE in semantic retrieval is highly dependent on the quality of expansion terms and the application of filtering strategies. QE is not inherently beneficial, but requires careful design to improve retrieval performance.