Fine-grained Entity Typing without Knowledge Base

Jing Qian, Yibin Liu, Lemao Liu, Yangming Li, Haiyun Jiang, Haisong Zhang, Shuming Shi


Abstract
Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision. However, the reliance on KB means this training setting can be hampered by the lack of or the incompleteness of the KB. To alleviate this limitation, we propose a novel setting for training FET models: FET without accessing any knowledge base. Under this setting, we propose a two-step framework to train FET models. In the first step, we automatically create pseudo data with fine-grained labels from a large unlabeled dataset. Then a neural network model is trained based on the pseudo data, either in an unsupervised way or using self-training under the weak guidance from a coarse-grained Named Entity Recognition (NER) model. Experimental results show that our method achieves competitive performance with respect to the models trained on the original KB-supervised datasets.
Anthology ID:
2021.emnlp-main.431
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5309–5319
Language:
URL:
https://aclanthology.org/2021.emnlp-main.431
DOI:
10.18653/v1/2021.emnlp-main.431
Bibkey:
Cite (ACL):
Jing Qian, Yibin Liu, Lemao Liu, Yangming Li, Haiyun Jiang, Haisong Zhang, and Shuming Shi. 2021. Fine-grained Entity Typing without Knowledge Base. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5309–5319, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Fine-grained Entity Typing without Knowledge Base (Qian et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.431.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.431.mp4
Code
 lemaoliu/fet-data
Data
FIGER