Learning Structured Representations of Entity Names using Active Learning and Weak Supervision

Kun Qian, Poornima Chozhiyath Raman, Yunyao Li, Lucian Popa


Abstract
Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representations of entity names without context and external knowledge is particularly challenging. In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem. Our experimental evaluation show that this framework enables the learning of high-quality models from merely a dozen or so labeled examples.
Anthology ID:
2020.emnlp-main.517
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6376–6383
Language:
URL:
https://aclanthology.org/2020.emnlp-main.517
DOI:
10.18653/v1/2020.emnlp-main.517
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.517.pdf
Video:
 https://slideslive.com/38938677
Code
 System-T/PARTNER