“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。”

Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text. However, FET poses a major challenge known as the noise labeling problem, whereby current methods rely on estimating noise distribution to identify noisy labels but are confused by diverse noise distribution deviation. To address this limitation, we introduce Co-Prediction Prompt Tuning for noise correction in FET, which leverages multiple prediction results to identify and correct noisy labels. Specifically, we integrate prediction results to recall labeled labels and utilize a differentiated margin to identify inaccurate labels. Moreover, we design an optimization objective concerning divergent co-predictions during fine-tuning, ensuring that the model captures sufficient information and maintains robustness in noise identification. Experimental results on three widely-used FET datasets demonstrate that our noise correction approach significantly enhances the quality of various types of training samples, including those annotated using distant supervision, ChatGPT, and crowdsourcing.

Named entity recognition (NER) is a fundamental task in natural language processing that aims to identify and classify named entities in text. However, span-based methods for NER typically assign entity types to text spans, resulting in an imbalanced sample space and neglecting the connections between non-entity and entity spans. To address these issues, we propose a novel approach for NER, named the Boundary Offset Prediction Network (BOPN), which predicts the boundary offsets between candidate spans and their nearest entity spans. By leveraging the guiding semantics of boundary offsets, BOPN establishes connections between non-entity and entity spans, enabling non-entity spans to function as additional positive samples for entity detection. Furthermore, our method integrates entity type and span representations to generate type-aware boundary offsets instead of using entity types as detection targets. We conduct experiments on eight widely-used NER datasets, and the results demonstrate that our proposed BOPN outperforms previous state-of-the-art methods.