Large Scaled Relation Extraction With Reinforcement Learning

Authors

  • Xiangrong Zeng Institute of Automation, Chinese Academy of Sciences
  • Shizhu He Institute of Automation, Chinese Academy of Sciences
  • Kang Liu Institute of Automation, Chinese Academy of Sciences
  • Jun Zhao Institute of Automation, Chinese Academy of Sciences

Keywords:

relation extraction, reinforcement learning, large scaled

Abstract

Sentence relation extraction aims to extract relational facts from sentences, which is an important task in natural language processing field. Previous models rely on the manually labeled supervised dataset. However, the human annotation is costly and limits to the number of relation and data size, which is difficult to scale to large domains. In order to conduct largely scaled relation extraction, we utilize an existing knowledge base to heuristically align with texts, which not rely on human annotation and easy to scale. However, using distant supervised data for relation extraction is facing a new challenge: sentences in the distant supervised dataset are not directly labeled and not all sentences that mentioned an entity pair can represent the relation between them. To solve this problem, we propose a novel model with reinforcement learning. The relation of the entity pair is used as distant supervision and guide the training of relation extractor with the help of reinforcement learning method. We conduct two types of experiments on a publicly released dataset. Experiment results demonstrate the effectiveness of the proposed method compared with baseline models, which achieves 13.36\% improvement.

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Published

2018-04-27

How to Cite

Zeng, X., He, S., Liu, K., & Zhao, J. (2018). Large Scaled Relation Extraction With Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11950