FSN:基于过分隔网络的联合实体和关系提取
Qicai Dai1,2, Wenzhong Yang1,2, Fuyuan Wei1,2
1School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China.
Entropy (Basel, Switzerland)
|February 23, 2024
概括
本研究引入了一种新的联合实体和关系提取方法,FSN,以解决子任务之间的特征不平衡. FSN 改进了信息过和特征合并,以便从复杂文本中更准确地进行关系三重提取.
科学领域:
- 自然语言处理自然语言处理.
- 提取信息 提取信息
- 机器学习 机器学习
背景情况:
- 联合实体和关系提取方法对于从文本中提取结构化信息至关重要.
- 现有的方法通常在命名实体识别 (NER) 和关系提取 (RE) 子任务之间存在不平衡的特征相互作用.
- 这种不平衡阻碍了在提取关系三倍数时的最佳性能.
研究的目的:
- 提出一种新的联合实体和关系提取方法,FSN,以解决NER和RE子任务之间的特征相互作用不平衡.
- 加强对NER和RE的局部特征信息的提取.
- 为了提高关系三重提取的整体精度.
主要方法:
- 开发了一个使用双向LSTM进行信息过和合并的过器分离器网络 (FSN) 模块.
- 引入了受变压器解码器和平均聚合启发的命名实体识别生成 (NERG) 和关系提取生成 (REG) 模块.
- 实现了一个动态损失函数,以适应性地调整子任务学习权重.
主要成果:
- 拟议的FSN模型在联合实体和关系提取方面表现得更好.
- 在NERG和REG模块有效地捕获实体和关系边界信息.
- 与基线模型相比,SciERC和ACE2005数据集的实验结果显示了令人满意的性能.
结论:
- FSN方法有效地减轻了联合实体和关系提取中的特征相互作用失衡.
- 新的NERG和REG模块增强了捕捉关键的当地特征.
- 动态损失函数有助于缩小理想和现实的提取结果之间的差距,为复杂文本分析提供了有前途的方法.
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