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This content will become publicly available on August 24, 2025

Title: Ontology Enrichment for Effective Fine-grained Entity Typing
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 given the massive scale of data. 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 categories of extra information: instance information for training sample augmentation and topic information to relate types with 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. OnEFET also enjoys strong transferability to unseen and finer-grained types. Code is available at https://github.com/ozyyshr/OnEFET.  more » « less
Award ID(s):
1956151
PAR ID:
10540605
Author(s) / Creator(s):
; ; ; ; ;
Editor(s):
Baeza-Yates, Ricardo; Bonchi, Francesco
Publisher / Repository:
ACM
Date Published:
Edition / Version:
1
ISBN:
9798400704901
Page Range / eLocation ID:
2318 to 2327
Subject(s) / Keyword(s):
Ontology Enrichment Fine-grained Entity Typing Text Mining NLP LLM NER Information Extraction
Format(s):
Medium: X
Location:
Barcelona Spain
Sponsoring Org:
National Science Foundation
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