保存语义单元信息和简化语法组成部分,以改善隐式话语关系识别.
Zhongyang Fang1, Yue Cong1, Yuhan Chai1
1The Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
隐式话语关系识别 (IDRR) 是由于缺少连接器而具有挑战性的. 新的语义单元嵌入方法通过保留文本含义来提高IDRR性能.
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:
- 隐式话语关系识别 (IDRR) 在浅层话语解析中是一个困难的任务.
- 由于没有明确的连接词,因此需要保持语义完整性,以便准确地预测关系.
- 当前的词级嵌入可能会通过对有意义的短语进行细分而导致语义损失.
研究的目的:
- 为改进IDRR提出和评估将句子细分成完整的语义单元的新方法.
- 调查语义单元嵌入与词级嵌入对IDRR性能的影响.
主要方法:
- 开发了三种句子细分方法:基于语料库 (基线),基于树的组成部分解析和基于树的依赖性解析.
- 在最先进的IDRR模型中实现语义单元嵌入.
- 与传统的词级嵌入相比,比较了拟议方法的性能.
主要成果:
- 与单词级嵌入相比,语义单元嵌入有效地保留了句子语义.
- 提出的细分和嵌入方法提高了IDRR模型的性能.
- 基于树的依赖性和构成分析方法提供灵活和自动的句子细分.
结论:
- 在语义单元层面的嵌入对于保存文本中的语义信息至关重要.
- 开发的方法提供了一种更有效的方法来实现隐性话语关系识别.
- 这项研究有助于推进话语解析和自然语言理解领域.
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