Ontology Enrichment for Effective Fine-grained Entity Typing

TL;DR

This paper introduces OnEFET, a zero-shot fine-grained entity typing method that enriches ontologies with additional information and employs a coarse-to-fine algorithm, significantly improving performance without human annotation.

Contribution

We propose a novel ontology enrichment and a coarse-to-fine algorithm for zero-shot entity typing, outperforming existing methods and reducing reliance on human annotation.

Findings

OnEFET outperforms existing zero-shot methods by a large margin.

OnEFET rivals supervised methods in fine-grained entity typing.

Enriching ontology nodes improves zero-shot entity classification accuracy.

Abstract

Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional methods for FET require extensive human annotation, which is time-consuming and costly. Recent studies have been developing weakly supervised or zero-shot approaches. We study the setting of zero-shot FET where only an ontology is provided. However, most existing ontology structures lack rich supporting information and even contain ambiguous relations, making them ineffective in guiding FET. Recently developed language models, though promising in various few-shot and zero-shot NLP tasks, may face challenges in zero-shot FET due to their lack of interaction with task-specific ontology. In this study, we propose OnEFET, where we (1) enrich each node in the ontology structure with two types of extra information: instance information for training sample augmentation and topic information to relate types to contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples. Our experiments show that OnEFET achieves high-quality fine-grained entity typing without human annotation, outperforming existing zero-shot methods by a large margin and rivaling supervised methods.