Neurosymbolic Information Extraction from Transactional Documents
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914192664035328 |
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| author | Hemmer, Arthur Coustaty, Mickaël Bartolo, Nicola Ogier, Jean-Marc |
| author_facet | Hemmer, Arthur Coustaty, Mickaël Bartolo, Nicola Ogier, Jean-Marc |
| contents | This paper presents a neurosymbolic framework for information extraction from documents, evaluated on transactional documents. We introduce a schema-based approach that integrates symbolic validation methods to enable more effective zero-shot output and knowledge distillation. The methodology uses language models to generate candidate extractions, which are then filtered through syntactic-, task-, and domain-level validation to ensure adherence to domain-specific arithmetic constraints. Our contributions include a comprehensive schema for transactional documents, relabeled datasets, and an approach for generating high-quality labels for knowledge distillation. Experimental results demonstrate significant improvements in $F_1$-scores and accuracy, highlighting the effectiveness of neurosymbolic validation in transactional document processing. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_09666 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neurosymbolic Information Extraction from Transactional Documents Hemmer, Arthur Coustaty, Mickaël Bartolo, Nicola Ogier, Jean-Marc Computation and Language This paper presents a neurosymbolic framework for information extraction from documents, evaluated on transactional documents. We introduce a schema-based approach that integrates symbolic validation methods to enable more effective zero-shot output and knowledge distillation. The methodology uses language models to generate candidate extractions, which are then filtered through syntactic-, task-, and domain-level validation to ensure adherence to domain-specific arithmetic constraints. Our contributions include a comprehensive schema for transactional documents, relabeled datasets, and an approach for generating high-quality labels for knowledge distillation. Experimental results demonstrate significant improvements in $F_1$-scores and accuracy, highlighting the effectiveness of neurosymbolic validation in transactional document processing. |
| title | Neurosymbolic Information Extraction from Transactional Documents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.09666 |