Kuang-Chih Lee
2018
Extracting Entities and Relations with Joint Minimum Risk Training
Changzhi Sun
|
Yuanbin Wu
|
Man Lan
|
Shiliang Sun
|
Wenting Wang
|
Kuang-Chih Lee
|
Kewen Wu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
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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Co-authors
- Changzhi Sun 1
- Yuanbin Wu 1
- Man Lan 1
- Shiliang Sun 1
- Wenting Wang 1
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- Kewen Wu 1