Extracting Entities and Relations with Joint Minimum Risk Training

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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Results from the Paper


 Ranked #1 on Relation Extraction on ACE 2005 (Sentence Encoder metric)

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Relation Extraction ACE 2005 MRT NER Micro F1 83.6 # 16
RE+ Micro F1 59.6 # 10
Sentence Encoder biLSTM # 1
Cross Sentence No # 1

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