{"ID":2866025,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21151","arxiv_id":"2509.21151","title":"Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction","abstract":"Relation extraction (RE) aims to identify semantic relations between entities in unstructured text. Although recent work extends traditional RE to multimodal scenarios, most approaches still adopt classification-based paradigms with fused multimodal features, representing relations as discrete labels. This paradigm has two significant limitations: (1) it overlooks structural constraints like entity types and positional cues, and (2) it lacks semantic expressiveness for fine-grained relation understanding. We propose \\underline{R}etrieval \\underline{O}ver \\underline{C}lassification (ROC), a novel framework that reformulates multimodal RE as a retrieval task driven by relation semantics. ROC integrates entity type and positional information through a multimodal encoder, expands relation labels into natural language descriptions using a large language model, and aligns entity-relation pairs via semantic similarity-based contrastive learning. Experiments show that our method achieves state-of-the-art performance on the benchmark datasets MNRE and MORE and exhibits stronger robustness and interpretability.","short_abstract":"Relation extraction (RE) aims to identify semantic relations between entities in unstructured text. Although recent work extends traditional RE to multimodal scenarios, most approaches still adopt classification-based paradigms with fused multimodal features, representing relations as discrete labels. This paradigm has...","url_abs":"https://arxiv.org/abs/2509.21151","url_pdf":"https://arxiv.org/pdf/2509.21151v1","authors":"[\"Lei Hei\",\"Tingjing Liao\",\"Yingxin Pei\",\"Yiyang Qi\",\"Jiaqi Wang\",\"Ruiting Li\",\"Feiliang Ren\"]","published":"2025-09-25T13:38:38Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.IR\"]","methods":"[\"Language Model\"]","has_code":false}