基于跨度和级联双解码的实体关系的联合提取模型
Tao Liao1, Haojie Sun1, Shunxiang Zhang1
1College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了一种新的联合实体关系提取模型,有效地识别重叠关系. 新模型利用跨度和级联双解码,提高了对基准数据集的提取精度.
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:
- 实体关系提取对于理解文本至关重要.
- 现有的模型在与重叠的关系中扎.
- 识别复杂的关系结构仍然是一个挑战.
研究的目的:
- 提出一个新的联合实体关系提取模型.
- 为了解决识别重叠关系的局限性.
- 为了提高实体关系三重提取的准确性.
主要方法:
- 一个新的模型,结合了基于跨度的处理和级联的双重解码.
- 使用来自变压器的双向编码器表示 (BERT) 来进行文本编码.
- 采用双向长短期内存 (Bi-LSTM) 网络进行实体解码.
主要成果:
- 拟议的模型有效地识别了重叠的关系.
- 实验结果显示F1得分有显著的改善.
- 在纽约时报和WebNLG数据集上表现优于现有的基线模型.
结论:
- 跨度和级联双重解码方法增强了实体关系提取.
- 该模型在处理复杂的关系数据方面表现出卓越的性能.
- 这种方法为重叠关系的识别提供了一个强大的解决方案.
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