基于变压器的变量类型化方法
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A Rapid Method for Modeling a Variable Cycle Engine
Published on: August 13, 2019
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Charles Arthel Rey1, Jose Lorenzo Danguilan1, Karl Patrick Mendoza1
1Chemical Engineering Intelligence Learning Laboratory, Department of Chemical Engineering, University of the Philippines Diliman, Quezon City, 1101 Philippines.
Heliyon
|October 16, 2023
概括
本研究引入了一种新的方法,用于科学大数据中的变量类型,使用基于BERT的实体识别和关系提取模型. 这些模型取得了有希望的结果,证明了改善对异质数学知识管理的潜力.
科学领域:
- 科学大数据管理科学大数据管理
- 科学中的自然语言处理.
- 数学知识表示 数学知识表示
背景情况:
- 大数据在科学研究中提出了重大挑战,特别是在管理异构的数学知识方面.
- 现有的可变打字方法是初步的,需要先进的解决方案.
- 数学数据的固有异质性使其在科学大数据中的管理变得复杂.
研究的目的:
- 实施一个端到端实体识别 (ER) 和关系提取 (RE) 方法来实现变量类型化.
- 评估基于BERT的模型对此任务的性能.
- 探索先进的NLP技术在科学数据管理中的潜力.
主要方法:
- 开发用于变量类型的微数据集.
- 应用BERT (变压器双向编码器表示) 模型用于ER和RE.
- 使用精度,回忆和F1分数评估模型性能.
主要成果:
- 该ER模型实现了0.8142的精度,0.7816的回忆和0.7975.5的F1-Score.
- 该RE模型实现了0.4919的精度,0.6030的回忆,以及0.5418.1的F1-Score.
- 尽管数据集有限,但性能与现有文献价值相当.
结论:
- 基于BERT的ER和RE方法显示了在科学大数据中有效的变量类型化潜力.
- 该研究强调了影响基于BERT的方法的因素,并建议了未来的研究方向.
- 用更大的数据集进行进一步开发可以加强对异质数学知识的管理.
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