小麦胚胎信息实体关系的联合提取基于深度字符和词汇融合
Xiaoxiao Jia1, Guang Zheng1,2, Chenyang Dong1
1College of Information and Management Sciences, Henan Agriculture University, Zhengzhou, 450046, China.
Scientific reports
|May 6, 2024
概括
一个新的模型,WGIE-DCWF,准确地从非结构化数据中提取小麦品种信息,改进遗传研究和育种. 这种方法通过解决实体边界和关系来增强对小麦生殖质的理解.
科学领域:
- 农业科学 农业科学
- 计算语言学 计算语言学
- 生物信息学是一种生物信息学.
背景情况:
- 准确的小麦品种数据对于生殖质信息至关重要.
- 非结构化数据在识别实体边界和关系方面存在挑战.
- 现有的方法在小麦品种数据中与模糊实体和重叠信息作斗争.
研究的目的:
- 开发一种用于提取小麦品种结构化表型和遗传特征的自动化方法.
- 为了解决模糊实体界限和重叠关系在非结构化的小麦品种批准数据中的局限性.
- 提出WGIE-DCWF模型,用于联合提取小麦胚胎信息实体和关系.
主要方法:
- 提出了WGIE-DCWF (基于深度字符和词汇融合的小麦胚胎细胞信息实体关系的联合提取模型) 模型.
- 使用BERT的Transformer编码器来深入融合词语语义和字符信息.
- 在三重提取层中实现了级联指针网络,以提取头实体,尾实体和关系.
主要成果:
- 在WGIE-DCWF模型中,在小麦生殖血数据集 (WGD) 和公开的DuIE数据集上都表现出了卓越的性能.
- 在评估数据集上实现了高性能和良好的概括能力.
- 成功提高了模型提取重叠关系的能力.
结论:
- WGIE-DCWF模型为结构化提取小麦品种信息提供了一个强大的解决方案.
- 为建立小麦生殖质信息知识库提供了宝贵的技术支持.
- 显著有利于小麦育种,遗传研究,种植管理和农业生产.
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