CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Large Scaled Relation Extraction with Reinforcement Learning
Zeng Xiangrong1,2; Liu Kang1,2; He Shizhu1,2; Zhao Jun1,2
2018
Conference NameProceedings of the 15th AAAI Conference on Artificial Intelligence (AAAI 2018)
Conference Date2018
Conference Place美国
Contribution Rank1
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.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20626
Collection模式识别国家重点实验室_自然语言处理
自然语言处理团队
Corresponding AuthorLiu Kang
Affiliation1.中国科学院大学
2.中科院自动化所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zeng Xiangrong,Liu Kang,He Shizhu,et al. Large Scaled Relation Extraction with Reinforcement Learning[C],2018.
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