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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.06861 |
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| _version_ | 1866910866812698624 |
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| author | Hei, Mengzhe Zhang, Zhouran Liu, Qingbao Pan, Yan Zhao, Xiang Peng, Yongqian Ye, Yicong Zhang, Xin Bai, Shuxin |
| author_facet | Hei, Mengzhe Zhang, Zhouran Liu, Qingbao Pan, Yan Zhao, Xiang Peng, Yongqian Ye, Yicong Zhang, Xin Bai, Shuxin |
| contents | Extracting high-quality structured information from scientific literature is crucial for advancing material design through data-driven methods. Despite the considerable research in natural language processing for dataset extraction, effective approaches for multi-tuple extraction in scientific literature remain scarce due to the complex interrelations of tuples and contextual ambiguities. In the study, we illustrate the multi-tuple extraction of mechanical properties from multi-principal-element alloys and presents a novel framework that combines an entity extraction model based on MatSciBERT with pointer networks and an allocation model utilizing inter- and intra-entity attention. Our rigorous experiments on tuple extraction demonstrate impressive F1 scores of 0.963, 0.947, 0.848, and 0.753 across datasets with 1, 2, 3, and 4 tuples, confirming the effectiveness of the model. Furthermore, an F1 score of 0.854 was achieved on a randomly curated dataset. These results highlight the model's capacity to deliver precise and structured information, offering a robust alternative to large language models and equipping researchers with essential data for fostering data-driven innovations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Multi-Tuple Extraction for Alloys: Integrating Pointer Networks and Augmented Attention Hei, Mengzhe Zhang, Zhouran Liu, Qingbao Pan, Yan Zhao, Xiang Peng, Yongqian Ye, Yicong Zhang, Xin Bai, Shuxin Computation and Language Artificial Intelligence Extracting high-quality structured information from scientific literature is crucial for advancing material design through data-driven methods. Despite the considerable research in natural language processing for dataset extraction, effective approaches for multi-tuple extraction in scientific literature remain scarce due to the complex interrelations of tuples and contextual ambiguities. In the study, we illustrate the multi-tuple extraction of mechanical properties from multi-principal-element alloys and presents a novel framework that combines an entity extraction model based on MatSciBERT with pointer networks and an allocation model utilizing inter- and intra-entity attention. Our rigorous experiments on tuple extraction demonstrate impressive F1 scores of 0.963, 0.947, 0.848, and 0.753 across datasets with 1, 2, 3, and 4 tuples, confirming the effectiveness of the model. Furthermore, an F1 score of 0.854 was achieved on a randomly curated dataset. These results highlight the model's capacity to deliver precise and structured information, offering a robust alternative to large language models and equipping researchers with essential data for fostering data-driven innovations. |
| title | Enhanced Multi-Tuple Extraction for Alloys: Integrating Pointer Networks and Augmented Attention |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.06861 |