@inproceedings{yang-etal-2019-learning,
title = "Learning Dynamic Context Augmentation for Global Entity Linking",
author = "Yang, Xiyuan and
Gu, Xiaotao and
Lin, Sheng and
Tang, Siliang and
Zhuang, Yueting and
Wu, Fei and
Chen, Zhigang and
Hu, Guoping and
Ren, Xiang",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1026",
doi = "10.18653/v1/D19-1026",
pages = "271--281",
abstract = "Despite of the recent success of collective entity linking (EL) methods, these {``}global{''} inference methods may yield sub-optimal results when the {``}all-mention coherence{''} assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space. In this paper, we propose a simple yet effective solution, called Dynamic Context Augmentation (DCA), for collective EL, which requires only one pass through the mentions in a document. DCA sequentially accumulates context information to make efficient, collective inference, and can cope with different local EL models as a plug-and-enhance module. We explore both supervised and reinforcement learning strategies for learning the DCA model. Extensive experiments show the effectiveness of our model with different learning settings, base models, decision orders and attention mechanisms.",
}
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<abstract>Despite of the recent success of collective entity linking (EL) methods, these “global” inference methods may yield sub-optimal results when the “all-mention coherence” assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space. In this paper, we propose a simple yet effective solution, called Dynamic Context Augmentation (DCA), for collective EL, which requires only one pass through the mentions in a document. DCA sequentially accumulates context information to make efficient, collective inference, and can cope with different local EL models as a plug-and-enhance module. We explore both supervised and reinforcement learning strategies for learning the DCA model. Extensive experiments show the effectiveness of our model with different learning settings, base models, decision orders and attention mechanisms.</abstract>
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%0 Conference Proceedings
%T Learning Dynamic Context Augmentation for Global Entity Linking
%A Yang, Xiyuan
%A Gu, Xiaotao
%A Lin, Sheng
%A Tang, Siliang
%A Zhuang, Yueting
%A Wu, Fei
%A Chen, Zhigang
%A Hu, Guoping
%A Ren, Xiang
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F yang-etal-2019-learning
%X Despite of the recent success of collective entity linking (EL) methods, these “global” inference methods may yield sub-optimal results when the “all-mention coherence” assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space. In this paper, we propose a simple yet effective solution, called Dynamic Context Augmentation (DCA), for collective EL, which requires only one pass through the mentions in a document. DCA sequentially accumulates context information to make efficient, collective inference, and can cope with different local EL models as a plug-and-enhance module. We explore both supervised and reinforcement learning strategies for learning the DCA model. Extensive experiments show the effectiveness of our model with different learning settings, base models, decision orders and attention mechanisms.
%R 10.18653/v1/D19-1026
%U https://aclanthology.org/D19-1026
%U https://doi.org/10.18653/v1/D19-1026
%P 271-281
Markdown (Informal)
[Learning Dynamic Context Augmentation for Global Entity Linking](https://aclanthology.org/D19-1026) (Yang et al., EMNLP-IJCNLP 2019)
ACL
- Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, and Xiang Ren. 2019. Learning Dynamic Context Augmentation for Global Entity Linking. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 271–281, Hong Kong, China. Association for Computational Linguistics.