发展一个抽象意义表示的管道方法,对动态神经网络进行解析
Florin Macicasan1, Alexandru Frasie1, Nicoleta-Teodora Vezan1
1Knowledge Engineering Research Group, Technical University of Cluj-Napoca, Cluj-Napoca 400027, Romania.
International journal of neural systems
|June 19, 2023
概括
这项研究通过将先进的依赖性解析技术集成到两级AMR解析器中来增强含义表示解析. 改进的重点是处理词汇之外的单词,优化关系识别,以便更好地提取文本意义.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 意义表示解析 (MRP) 旨在通过构建指向,循环图 (DAG) 来从文本中提取语义意义.
- 现有的MRP系统往往面临的挑战是词汇之外的单词和优化复杂的解析管道.
研究的目的:
- 通过结合最先进的依赖性解析技术来增强一个双阶段的抽象含义表示 (AMR) 解析器.
- 改进对外词汇的处理和AMR解析管道内关系识别的性能.
主要方法:
- 利用了指针生成器网络,改进了词和字符级别的嵌入,用于概念识别,解决词汇库之外的单词.
- 在关系识别模块中联合训练的Heads Selection和Arcs标记组件以提高性能.
- 探索了动态计算图形构建,作为实现端到端训练的静态方法的替代方案.
主要成果:
- 通过指针生成器网络和增强的嵌入实现了词汇之外的词的概念识别的改进.
- 通过共同优化其子组件,提高了关系识别模块的性能.
- 展示了动态构造的潜力,使复杂的解析管道能够进行端到端的培训.
结论:
- 集成先进的依赖分析技术显著提高了AMR分析性能.
- 动态计算图形构造为实现MRP端到端培训提供了一个有希望的方向.
- 提议的改进有助于从文本中提取更强大,更准确的含义.
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