OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing
| dc.contributor.author | Komarlu, Tanay | en |
| dc.contributor.author | Jiang, Minhao | en |
| dc.contributor.author | Wang, Xuan | en |
| dc.contributor.author | Han, Jiawei | en |
| dc.date.accessioned | 2024-09-04T12:15:18Z | en |
| dc.date.available | 2024-09-04T12:15:18Z | en |
| dc.date.issued | 2024-08-25 | en |
| dc.date.updated | 2024-09-01T07:48:27Z | en |
| dc.description.abstract | Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, is a basic but important task for knowledge extraction from unstructured text. FET has been studied extensively in natural language processing and typically relies on human-annotated corpora for training, which is costly and difficult to scale. Recent studies explore the utilization of pre-trained language models (PLMs) as a knowledge base to generate rich and context-aware weak supervision for FET. However, a PLM still requires direction and guidance to serve as a knowledge base as they often 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 annotation-free, 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 as well as a typical LLM method, ChatGPT. Our error analysis shows that refinement of the existing ontology structures will further improve fine-grained entity typing. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1145/3637528.3671745 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/121071 | en |
| dc.language.iso | en | en |
| dc.publisher | ACM | en |
| dc.rights | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International | en |
| dc.rights.holder | The author(s) | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en |
| dc.title | OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |