@inproceedings{sun-etal-2018-extracting,
title = "Extracting Entities and Relations with Joint Minimum Risk Training",
author = "Sun, Changzhi and
Wu, Yuanbin and
Lan, Man and
Sun, Shiliang and
Wang, Wenting and
Lee, Kuang-Chih and
Wu, Kewen",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1249/",
doi = "10.18653/v1/D18-1249",
pages = "2256--2265",
abstract = "We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances."
}
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<abstract>We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances.</abstract>
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%0 Conference Proceedings
%T Extracting Entities and Relations with Joint Minimum Risk Training
%A Sun, Changzhi
%A Wu, Yuanbin
%A Lan, Man
%A Sun, Shiliang
%A Wang, Wenting
%A Lee, Kuang-Chih
%A Wu, Kewen
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F sun-etal-2018-extracting
%X We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances.
%R 10.18653/v1/D18-1249
%U https://aclanthology.org/D18-1249/
%U https://doi.org/10.18653/v1/D18-1249
%P 2256-2265
Markdown (Informal)
[Extracting Entities and Relations with Joint Minimum Risk Training](https://aclanthology.org/D18-1249/) (Sun et al., EMNLP 2018)
ACL
- Changzhi Sun, Yuanbin Wu, Man Lan, Shiliang Sun, Wenting Wang, Kuang-Chih Lee, and Kewen Wu. 2018. Extracting Entities and Relations with Joint Minimum Risk Training. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2256–2265, Brussels, Belgium. Association for Computational Linguistics.