Entity matching, also known as entity resolution or record linkage, is a critical process in data integration, wherein the objective is to identify and reconcile different records that correspond to the same real-world entity across heterogeneous and often noisy data sources. Traditional approaches often rely on structured schemas or attribute alignment, limiting their applicability in contexts where entity descriptions are unstructured or inconsistent. We present PUFFME (Probabilistic Unkeyed Feature Fusion for Matching Entities), a novel method designed to operate directly on unstructured, semantic-free, entity attributes represented as bag-of-words. PUFFME models similarity using probabilistic distributions over textual features, enabling robust and interpretable comparisons between entities without requiring schema alignment or manual feature engineering. Evaluated on standard entity matching benchmarks, PUFFME achieves state-of-the-art performance on some datasets, significantly surpassing established baselines in others. These results highlight PUFFME’s effectiveness in tackling entity matching tasks under minimal assumptions and with high accuracy, offering a practical alternative for data integration tasks where traditional methods struggle.