@inproceedings{qian-etal-2020-learning,
title = "Learning Structured Representations of Entity Names using {A}ctive {L}earning and Weak Supervision",
author = "Qian, Kun and
Chozhiyath Raman, Poornima and
Li, Yunyao and
Popa, Lucian",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.517",
doi = "10.18653/v1/2020.emnlp-main.517",
pages = "6376--6383",
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.",
}
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%0 Conference Proceedings
%T Learning Structured Representations of Entity Names using Active Learning and Weak Supervision
%A Qian, Kun
%A Chozhiyath Raman, Poornima
%A Li, Yunyao
%A Popa, Lucian
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F qian-etal-2020-learning
%X 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.
%R 10.18653/v1/2020.emnlp-main.517
%U https://aclanthology.org/2020.emnlp-main.517
%U https://doi.org/10.18653/v1/2020.emnlp-main.517
%P 6376-6383
Markdown (Informal)
[Learning Structured Representations of Entity Names using Active Learning and Weak Supervision](https://aclanthology.org/2020.emnlp-main.517) (Qian et al., EMNLP 2020)
ACL