Hybrid Learning-to-Rank Approach for Complex Information Retrieval Systems

Biomedical question answering presents significant challenges due to the complexity of biomedical language and the need for precise information retrieval. This study aims to improve the performance of a biomedical information retrieval system through a hybrid learning-to-rank framework. Specifically, we combine lexical (BM25) and semantic (BioBERT) representations to form hybrid inputs for RankFormer, a transformer-based ranking model. This hybrid representation captures both surface-level term matching and deep contextual understanding. Experiments conducted on the BioASQ dataset show that our approach achieves better ranking performance compared to the standalone lexical or neural baselines, reaching a MAP@10 of 0.9614 and an nDCG@10 of 0.9320. These results highlight the effectiveness of hybrid input representations in enhancing biomedical answer ranking.