This Masters thesis aims to study and implement modern neural network architectures for record matching involving data that contain both structured fields (e.g., names, dates, numerical values) and semi-structured or unstructured information (e.g., descriptions, titles, excerpts, or even images).
The proposed approach is based on designing an architecture that combines:
- Semantic embeddings of structured fields and textual content,
- Attention mechanisms to highlight important features, and
- Information aggregation mechanisms for entire records.
The methodological framework is informed by relevant literature, including:
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