General-purpose text embedding models are designed to capture semantic similarity but are not optimised for distinguishing entity records that represent the same real-world business or person. This limitation affects applications such as entity resolution and duplicate record retrieval, where small textual differences may either preserve or change identity. This paper investigates whether domain-specific triplet fine-tuning can adapt pretrained embedding models for identity-sensitive retrieval. A synthetic dataset of business and person records was created with identity-preserving variations and challenging non-matching examples. Two widely used embedding models were evaluated before and after fine-tuning using a margin-based similarity evaluation. The results show substantial improvements in separating true matches from highly similar non-matches, demonstrating that domain-specific triplet training can effectively reshape general-purpose embedding spaces for entity retrieval. These findings suggest that targeted fine-tuning provides a practical approach for improving embedding models in data quality management and information retrieval applications.
A transformer-based fully fine-tuned XLNet model with 117M parameters is proposed, designed to recognize and extract named entities form general domain data and can be used for content recommendation, information retrieval in Q&A systems.
Parth Goel, A. Bhatt, Biswajeet Pradhan et al.· PeerJ Computer Science· 0 citations
The factors underlying performance differences across matcher architectures are clarified and motivate future research and benchmark designs that better disentangle architectural choices from model-level factors while explicitly evaluating distribution shift and cross-dataset transferability.
Zeyu Zhang, Xue Li, Iacer Calixto et al.· 0 citations
The proposed IRIS (Identity Representations from Internal States), a training-free framework that constructs for each entity an iris-like signature encoding its distinctive and stable identity characteristics, thereby forming a shared space in which each entity is encoded once and can be aligned across different KGs through direct similarity comparison.
Xinran Liu, Shengtao Li, Shouqian Shi et al.· 0 citations
H+ Embedding is introduced, a unified multi-granularity retriever that predicts variable-length phrase partitions, preserves uncovered tokens as singletons, and applies importance-guided unit selection with weighted MaxSim interaction.
Shusen Zhang, Junyi Hu, Ye Feng et al.· 0 citations
KoVRE: Korean Visual Document Retrieval Embedding is introduced, a single-vector retriever for Korean visual documents, alongside a comprehensive training recipe, demonstrating that targeted bilingual supervision and carefully designed training strategies can produce a highly effective Korean VDR model across diverse document domains, without requiring a scaled-up backbone or multi-vector representations.
The Semantic-Aware Fine-Tuning (SAFT) framework is proposed to address semantic compression in specific domains, which incorporates Semantic-Aware Soft-Label Supervision and Intra-modal Structural Distillation to establish a promising paradigm for domain-specific TBIR tasks.
Jingyang Tan, Shengan Yang, Yuanpeng Chen et al.· 0 citations