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Best from Top k Versus Top 1: Improving Distant Supervision Relation Extraction with Deep Reinforcement Learning

  • Yaocheng Gui
  • Qian Liu
  • Tingming Lu
  • Zhiqiang GaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

Abstract

Distant supervision relation extraction is a promising approach to find new relation instances from large text corpora. Most previous works employ the top 1 strategy, i.e., predicting the relation of a sentence with the highest confidence score, which is not always the optimal solution. To improve distant supervision relation extraction, this work applies the best from top k strategy to explore the possibility of relations with lower confidence scores. We approach the best from top k strategy using a deep reinforcement learning framework, where the model learns to select the optimal relation among the top k candidates for better predictions. Specifically, we employ a deep Q-network, trained to optimize a reward function that reflects the extraction performance under distant supervision. The experiments on three public datasets - of news articles, Wikipedia and biomedical papers - demonstrate that the proposed strategy improves the performance of traditional state-of-the-art relation extractors significantly. We achieve an improvement of 5.13% in average F\(_1\)-score over four competitive baselines.

Keywords

Distant supervision Relation extraction Deep reinforcement learning Deep Q-networks 

Notes

Acknowledgements

This work is partially funded by the National Science Foundation of China under Grant 61170165, Grant 61702279, Grant 61602260, and Grant 61502095.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yaocheng Gui
    • 1
  • Qian Liu
    • 2
  • Tingming Lu
    • 1
  • Zhiqiang Gao
    • 1
    Email author
  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.School of Computer Science and TechnologyNanjing University of Posts and TelecommunicationsNanjingChina

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