Abstract
Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, will play an important role in natural language understanding. A supervised FET method, which typically relies on human-annotated corpora for training, is costly and difficult to scale. Recent studies leverage pre-trained language models (PLMs) to generate rich and context-aware weak supervision for FET. However, a PLM may still generate a mixture of rough and fine-grained types, or tokens unsuitable for typing. In this study, we vision that an ontology provides a semantics-rich, hierarchical structure, which will help select the best results generated by multiple PLM models and head words. Specifically, we propose a novel zero-shot, ontology-guided FET method, OntoType, which follows a type ontological structure, from coarse to fine, ensembles multiple PLM prompting results to generate a set of type candidates, and refines its type resolution, under the local context with a natural language inference model. Our experiments on the Ontonotes, FIGER, and NYT datasets using their associated ontological structures demonstrate that our method outperforms the state-of-the-art zero-shot fine-grained entity typing methods. Our error analysis shows that refinement of the existing ontology structures will further improve fine-grained entity typing.
Abstract (translated)
Fine-grained entity typing (FET)，即在文本中为具有上下文敏感、精细语义类型的实体分配角色，将在自然语言理解中发挥重要作用。通常依赖人工标注的 Corpus 进行训练的 supervised FET 方法成本较高且难以扩展。最近的研究利用预训练语言模型(PLMs)生成丰富的、具有上下文意识的弱监督结果，但一个 PLM 可能仍然生成粗糙和精细类型的混合或不适合 typing 的 token。在本文中，我们愿景一个零次元、基于本体论的指导的 FET 方法，名为 OntoType。该方法遵循类型的本体论结构，从粗到细，结合多个 PLM prompt 结果生成一组类型候选人，并优化其类型分辨率，在一个自然语言推理模型 local 上下文下。我们对 Ontonotes、FigER 和 New York Times 数据集使用其相关的本体论结构进行了实验，证明了我们的方法和最先进的零次元精细实体 typing 方法相比表现更好。我们的错误分析表明，改进现有的本体论结构将进一步提高 Fine-grained entity typing。
URL
https://arxiv.org/abs/2305.12307
