“命名实体识别旨在自动识别出文本中具有特定意义的实体(例如,人名、地名),古籍文献中的命名实体识别通过识别人名、书籍、官职等实体,为深度挖掘、组织古汉语人文知识提供重要支撑。现有的中文命名实体识别方法主要聚焦在现代文,但古籍中的实体识别具有更大的挑战,表现在实体的歧义性和边界模糊性两方面。由于古籍行文简练,单字表达加剧了实体的歧义性问题,句读及分词断句难度的提升使实体边界的识别更具挑战性。为有效处理上述问题,本文提出一种基于信息论及篇章信息的古籍命名实体识别方法。通过检索古籍文本的来源信息融入篇章先验知识,并在同一篇章的古籍文本上采取滑动窗口采样增强,以引入篇章背景信息,有效缓解实体歧义性问题。此外,在信息论视角下,约束实体的上下文信息及实体本身特征的编码,最大程度保留泛化特征,去除冗余信息,缓解实体边界模糊的问题,在词义复杂多样、句读困难的古文典籍中提升命名实体识别性能。最终,在token-wise和span-level感知的命名实体识别基础框架下,本文的方法取得了最优的评测性能。”

Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision. Typically, the wrongly labeled instances comprise numbers of incomplete and inaccurate annotations, while most prior denoising works are only concerned with one kind of noise and fail to fully explore useful information in the training set. To address this issue, we propose a robust learning paradigm named Self-Collaborative Denoising Learning (SCDL), which jointly trains two teacher-student networks in a mutually-beneficial manner to iteratively perform noisy label refinery. Each network is designed to exploit reliable labels via self denoising, and two networks communicate with each other to explore unreliable annotations by collaborative denoising. Extensive experimental results on five real-world datasets demonstrate that SCDL is superior to state-of-the-art DS-NER denoising methods.

Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision. Typically, the wrongly labeled instances comprise numbers of incomplete and inaccurate annotations, while most prior denoising works are only concerned with one kind of noise and fail to fully explore useful information in the training set. To address this issue, we propose a robust learning paradigm named Self-Collaborative Denoising Learning (SCDL), which jointly trains two teacher-student networks in a mutually-beneficial manner to iteratively perform noisy label refinery. Each network is designed to exploit reliable labels via self denoising, and two networks communicate with each other to explore unreliable annotations by collaborative denoising. Extensive experimental results on five real-world datasets demonstrate that SCDL is superior to state-of-the-art DS-NER denoising methods.