Enhanced Multi-Tuple Extraction for Alloys: Integrating Pointer Networks and Augmented Attention

• URL: http://arxiv.org/abs/2503.06861v1
• Date: Mon, 10 Mar 2025 02:39:06 GMT
• Title: Enhanced Multi-Tuple Extraction for Alloys: Integrating Pointer Networks and Augmented Attention
• Authors: Mengzhe Hei, Zhouran Zhang, Qingbao Liu, Yan Pan, Xiang Zhao, Yongqian Peng, Yicong Ye, Xin Zhang, Shuxin Bai,
• Abstract summary: We present a novel framework that combines an extraction model based on MatSciBERT with pointer and an allocation model.<n>Our experiments on extraction demonstrate impressive F1 scores of 0.947, 0.93 and 0.753 across datasets.<n>These results highlight the model's capacity to deliver precise and structured information.
• Score: 6.938202451113495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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